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Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

Motivation

According to a recent report, overall tobacco use increased in youths (middle school and high school students) in the United States in 2017 and 2018, despite previous years of declining use.

This major increase is attributed to an increase in the use of electronic cigarette (e-cigarette) products.

E-cigarettes are referred to by many different names, including but not limited to:

  1. Electronic nicotine delivery systems (ENDS)
  2. Vapes
  3. e-hookahs
  4. vape pens
  5. tanks
  6. mods

The devices vary greatly:

[source]

See this CDC guide and the American Lung Association website for more information.

The report found that:

During 2017–2018, current use of any tobacco product increased 38.3% (from 19.6% to 27.1%) among high school students and 28.6% (from 5.6% to 7.2%) among middle school students; e-cigarette use increased 77.8% (from 11.7% to 20.8%) among high school students and 48.5% (from 3.3% to 4.9%) among middle school students.

In 2018, the Federal Drug Administration (FDA) in the United States stated that e-cigarette usage use among youth reached:

“nothing short of an epidemic proportion of growth”

In this case study, we will be investigating the same data used in the report that generated the above findings. This data comes from the The National Youth Tobacco Survey (NYTS).

Gentzke, Andrea S., Melisa Creamer, Karen A. Cullen, Bridget K. Ambrose, Gordon Willis, Ahmed Jamal, and Brian A. King. “Vital Signs: Tobacco Product Use Among Middle and High School Students - United States, 2011-2018.” MMWR. Morbidity and Mortality Weekly Report 68 (6): 157–64 (2019).

Main Questions

Our main question:

  1. How has tobacco/nicotine and e-cigarette use by American youths changed since 2015?
  2. How do vaping rates compare between males and females?

  3. What vaping brands and flavors appear to be used the most frequently?
    We will base this on the following survey questions:
    > “During the past 30 days, what brand of e-cigarettes did you usually use?”
    >" What flavors of tobacco products have you used in the past 30 days?"

  4. Have vaping rates possibly influenced tobacco/nicotine use?

Learning Objectives

In this case study, we will cover how to import data from multiple files efficiently, how to import data from excel files, and how to make a variety of visualizations to compare multiple groups across time. We will also demonstrate how to work with codebooks. We will cover the concept of survey weighting and introduce the srvyr package. We will discuss the difference between pooled cross-sectional data and panel data. We will especially focus on using packages and functions from the Tidyverse for wrangling data, such as tidyr and dplyr and for visualization, such as as ggplot2. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.

We will begin by loading the packages that we will need:

Package Use
here to easily load and save data
readxl to import the data in the excel files
stringr to manipulate the character strings within the data
purrr to import the data in all the different excel and csv files efficiently
dplyr to arrange/filter/select/compare specific subsets of the data
readr to import the CSV file data
tidyr to rearrange data in wide and long formats
ggplot2 to make visualizations with multiple layers
scales to allow us to look at the colors within the viridis package
viridis to make plots with a color palette that is compatible with color blindness
forcats to allow for reordering of factors in plots
syrvr to use survey weights
cowplot to allow plots to be combined

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

Context

According to the cited Morbidity and Mortality Weekly Report this was what was already known about this topic and the implications of this study:

Importantly, the vapors used in e-cigarettes contain harmful chemicals:

source

E-cigarette usage has also been associated with lung injury:

source

See here for additional information about the potential health effects of e-cigarettes in teens and young adults.

Limitations

There are some important considerations regarding this data analysis to keep in mind:

  1. The data included in the National Youth Tobacco Survey (NYTS) does not follow the same individual students over time. A longitudinal study that does follow the same individuals over time collects data called panel data. The data in this study is called pooled cross-sectional data, and is obtained from random collection of observations across time.

According to Wikipedia:

Panel data differs from pooled cross-sectional data across time, because it deals with the observations on the same subjects in different times whereas the latter observes different subjects in different time periods

  1. The data include percentages of students reporting use of each particular tobacco product, but the survey questions did not ask the relative amount of use of one product compared to another. For example the survey included questions like:“What flavors of tobacco products have you used in the past 30 days?” but did not ask how often one flavor was used by the same individual over another.

While gender and sex are not actually binary, the data used in this analysis only contain information for groups of individuals who answered the survey questions as male or female.

What are the data?

The data in this case study comes from the National Youth Tobacco Survey (NYTS) which is an annual survey that asks students in high school and middle school (grades 6-12) about tobacco usage in the United States of America.

The data for this survey is freely available online at this website with data from 1999, 2000, 2002, 2004, 2006, 2009, and 2011-2019. We will be using data from 2015-2019 due to the fact that these years are the most recent that asked questions regarding e-cigarette usage.

Each year includes documentation, such as a codebook and an excel file containing the data:

Therefore, since we are using data from 2015-2019, the data we are interested in are located in 5 different excel files, one for each year, each with its own codebook.

The codebook contains information describing the data within the excel file.

As you can see the excel file contains very short variable names and numeric values, and it is not clear what they mean without the codebook:

The codebook explains what the variables (the columns) are:

And the codebook explains what the values for each variable are:

We will explain more later about what the values on the right indicate.

The reason that there are codebooks for each year is because the questions asked each year varied slightly.

The data in this survey are pooled cross-sectional data. In this study design, different subsets of students are surveyed each year and it is not clear which, if any, individuals participate more than once from one year to the next.

Data Import

Reading in the excel files

Since these excel files are so large (each has roughly 20,000 rows), it takes a bit of time for the data to load. To make the process faster, we previously imported these files, selected only the columns relevant to our questions of interest, and saved these data subsets as comma-separated (.csv) files.

Click here for details on how the data were originally imported

First we created a vector of file names of all the different excel files. Using the here() function of the here package, we looked in all the directories of the project. The list.files() function looked for all files with .xlsx within these sub-directories.

[1] "docs/2015-nyts-dataset-and-codebook-microsoft-excel/nyts2015.xlsx"
[2] "docs/2016-nyts-dataset-and-codebook-microsoft-excel/nyts2016.xlsx"
[3] "docs/2017-nyts-dataset-and-codebook-microsoft-excel/nyts2017.xlsx"
[4] "docs/2018-nyts-dataset-and-codebook-microsoft-excel/nyts2018.xlsx"
[5] "docs/2019-nyts-dataset-and-codebook-microsoft-excel/nyts2019.xlsx"

All the files were read using read_excel() of the readxl package. Using the map() function of the purrr package this was done efficiently for all of the excel files in the vector using one command. The . is used to indicate that we want to apply the read_excel() function to each element of the data that we just piped into the map() function.

Here we also used the %>% pipe which can be used to pass the input from one function to another for later sequential steps.

This created a single list of tibbles (one for each file).

The elements of this list are in the same order as the elements of the excel_files vector, so we can extract the data for a particular file (year) by selecting a certain element of the list. However, it is safer to be able to select the data from this list for a specific year using a name based on the original vector of file names. We extracted a name from each Excel file name using the str_extract() function of the stringr package. Here we are keeping occurrences of the character string “nyts201” followed by a “5”,“6”,“7”,“8”, or “9”.

[1] "nyts2015" "nyts2016" "nyts2017" "nyts2018" "nyts2019"

These names became the names of the tibbles in the list of tibbles.

Specific columns were selected using the select() function of dplyr from each of the tibbles using the variable name, as identified in the codebook as being of interest for our analysis. In some cases functions like starts_with() of the dplyr package were used to select several variables at once. Most of the survey questions about tobacco use start with an "E" or a "C" according to the codebooks.

tbl_files[["nyts2015"]] <- tbl_files[["nyts2015"]] %>%
    dplyr::select(psu, #Primary Sampling Unit
                  finwgt,#Analysis Weight
                  stratum,#Sampling stratum
                  Qn1, #Age
                  Qn2, #Sex
                  Qn3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  )


tbl_files[["nyts2016"]] <- tbl_files[["nyts2016"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  ) 

tbl_files[["nyts2017"]] <- tbl_files[["nyts2017"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  )

tbl_files[["nyts2018"]] <- tbl_files[["nyts2018"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  )

tbl_files[["nyts2019"]] <- tbl_files[["nyts2019"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q40, #Brand, e-cigarettes
                  Q62A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q62B, #Clove or spice
                  Q62C, #Fruit 
                  Q62D, #Chocolate
                  Q62E, #Alcoholic Drink
                  Q62F, #Candy/Desserts/Other Sweets
                  Q62G, #Some Other Flavor Not Listed Here 
                  )

A directory called data_reduced was created for the new .csv files using the base dir.create() function. A csv file was created for each of the tibbles in the list using the write_csv() function of the readr package. This was done all at once using the base mapply() function.

Now we will show how to read in the data from the five csv files that were created from the five different Excel files.

Data Exploration and Wrangling

Our goal in this section is to adjust or “wrangle” the data from each year into a common format so that we can combine the data sets across years for our analysis, and so that we have values in our variables that are correct and easy to interpret. We will need to understand what is the same and what is different across the data from different years, rename and recode the variables (e.g., by replacing the numbers 1 and 2 with the values “Male” and “Female” for the Sex variable), and combine the data. We will walk through these steps below.

First, let’s take a look at our data. We can get a good sense of it using the glimpse() function of the dplyr package.

Rows: 17,711
Columns: 29
$ psu        <chr> "015438", "015438", "015438", "015438", "015438", "015438"…
$ finwgt     <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745, 264.8745…
$ stratum    <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "B…
$ Qn1        <dbl> 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
$ Qn2        <dbl> 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2…
$ Qn3        <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5…
$ ECIGT      <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1…
$ ECIGAR     <dbl> 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2…
$ ESLT       <dbl> 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EELCIGT    <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1…
$ EROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2…
$ EFLAVCIGTS <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EBIDIS     <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EFLAVCIGAR <dbl> 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2…
$ EHOOKAH    <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EPIPE      <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ ESNUS      <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EDISSOLV   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CCIGT      <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CCIGAR     <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CSLT       <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CELCIGT    <dbl> 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CFLAVCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CBIDIS     <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CHOOKAH    <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CPIPE      <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CSNUS      <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CDISSOLV   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…

Updating the set of variables and their names

The easiest way of making it so that the data from the different years can be combined is by making sure the different data sets all contain the same variables that share the same names. In addition, giving the columns informative names will help make our code more readable. Currently, it isn’t very clear what most of the variables indicate since the variable names are uninformative on their own, without the code book.

We want to rename variables like Qn1 to something more meaningful like Age.

To do this we will use the rename() function of the dplyr package. The new name is always listed first before the =. This function will replace the old variable names with the new ones, i.e., after running the code below, there will no longer be a Qn1 variable in the data set, but there will be an Age variable instead. We will start working with the 2015 data, and then move on to the other years down below.

We also need to add new variables about usage of brands and specific flavors of e-cigarettes, because although later years have questions about this, this year unfortunately only has broad questions about flavors. Eventually we want to put the data from each year together, so we need the same variables for each year. Thus we need to add variables for brand and flavor to the 2015 data set that just contain missing values (NA).

To create these new variables, we will use the mutate function of the dplyr package. The mutate function can also be used to change the existing variables and create new variables from the old ones.

Now we can see how the data has changed:

Rows: 17,711
Columns: 38
$ psu                  <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt               <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum              <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age                  <dbl> 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1…
$ Sex                  <dbl> 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, …
$ Grade                <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, …
$ ECIGT                <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, …
$ ECIGAR               <dbl> 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ESLT                 <dbl> 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EELCIGT              <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, …
$ EROLLCIGTS           <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, …
$ EFLAVCIGTS           <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EBIDIS               <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EFLAVCIGAR           <dbl> 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, …
$ EHOOKAH              <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EPIPE                <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ESNUS                <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EDISSOLV             <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CCIGT                <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CCIGAR               <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CSLT                 <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CELCIGT              <dbl> 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CROLLCIGTS           <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CFLAVCIGTS           <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CBIDIS               <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CHOOKAH              <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CPIPE                <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CSNUS                <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CDISSOLV             <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ brand_ecig           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

The data for 2016-2018 have many common attributes, so we will want to write code that can be applied to all three data sets. To do this, we will use a function in R, which is basically a piece of code that can be applied to similar but different objects in R (e.g., the data tibbles from each of these three years). For more information on functions, see for example here.

These next 3 years have the same structure for many of the questions we are interested in. For example, they all have flavor questions, but not a brand question. Moreover, their variable names are consistent across the years; for each year, we want to replace the vague question name Q50A with the value menthol in all three data sets, and the same is true for the other flavor variables. For each of these years, we also want to create a variable brand_ecig that just contains NA values.

Since we want to perform the same modifications on the data from all three years, rather than repeating the same somewhat messy piece of code three times, we can do this more efficiently if we create a function to do all of these steps at once. Then we can use the map_at() function of the purrr package to apply the function we created (for renaming variables etc.) to the data from 2016-2018. By using vars() inside of the map_at() function we can specify what tibbles within our nyts_data list we want to include or exclude.

The final year, 2019, has a slightly different data structure compared to these earlier data sets. For example, it actually has a brand_ecig variable already, but does not contain a no_use variable. So we can’t use the same function for this data set, but have to write an individual code chunk.

Now let’s take a look at the variable names for each of the years using the map function from purrr.

$nyts2015
 [1] "psu"                  "finwgt"               "stratum"             
 [4] "Age"                  "Sex"                  "Grade"               
 [7] "ECIGT"                "ECIGAR"               "ESLT"                
[10] "EELCIGT"              "EROLLCIGTS"           "EFLAVCIGTS"          
[13] "EBIDIS"               "EFLAVCIGAR"           "EHOOKAH"             
[16] "EPIPE"                "ESNUS"                "EDISSOLV"            
[19] "CCIGT"                "CCIGAR"               "CSLT"                
[22] "CELCIGT"              "CROLLCIGTS"           "CFLAVCIGTS"          
[25] "CBIDIS"               "CHOOKAH"              "CPIPE"               
[28] "CSNUS"                "CDISSOLV"             "brand_ecig"          
[31] "menthol"              "clove_spice"          "fruit"               
[34] "chocolate"            "alcoholic_drink"      "candy_dessert_sweets"
[37] "other"                "no_use"              

$nyts2016
 [1] "psu"                  "finwgt"               "stratum"             
 [4] "Age"                  "Sex"                  "Grade"               
 [7] "ECIGT"                "ECIGAR"               "ESLT"                
[10] "EELCIGT"              "EHOOKAH"              "EROLLCIGTS"          
[13] "EFLAVCIGAR"           "EPIPE"                "ESNUS"               
[16] "EDISSOLV"             "EBIDIS"               "CCIGT"               
[19] "CCIGAR"               "CSLT"                 "CELCIGT"             
[22] "CHOOKAH"              "CROLLCIGTS"           "CPIPE"               
[25] "CSNUS"                "CDISSOLV"             "CBIDIS"              
[28] "menthol"              "clove_spice"          "fruit"               
[31] "chocolate"            "alcoholic_drink"      "candy_dessert_sweets"
[34] "other"                "no_use"               "brand_ecig"          

$nyts2017
 [1] "psu"                  "finwgt"               "stratum"             
 [4] "Age"                  "Sex"                  "Grade"               
 [7] "ECIGT"                "ECIGAR"               "ESLT"                
[10] "EELCIGT"              "EHOOKAH"              "EROLLCIGTS"          
[13] "EPIPE"                "ESNUS"                "EDISSOLV"            
[16] "EBIDIS"               "CCIGT"                "CCIGAR"              
[19] "CSLT"                 "CELCIGT"              "CHOOKAH"             
[22] "CROLLCIGTS"           "CPIPE"                "CSNUS"               
[25] "CDISSOLV"             "CBIDIS"               "menthol"             
[28] "clove_spice"          "fruit"                "chocolate"           
[31] "alcoholic_drink"      "candy_dessert_sweets" "other"               
[34] "no_use"               "brand_ecig"          

$nyts2018
 [1] "psu"                  "finwgt"               "stratum"             
 [4] "Age"                  "Sex"                  "Grade"               
 [7] "ECIGT"                "ECIGAR"               "ESLT"                
[10] "EELCIGT"              "EHOOKAH"              "EROLLCIGTS"          
[13] "EPIPE"                "ESNUS"                "EDISSOLV"            
[16] "EBIDIS"               "CCIGT"                "CCIGAR"              
[19] "CSLT"                 "CELCIGT"              "CHOOKAH"             
[22] "CROLLCIGTS"           "CPIPE"                "CSNUS"               
[25] "CDISSOLV"             "CBIDIS"               "menthol"             
[28] "clove_spice"          "fruit"                "chocolate"           
[31] "alcoholic_drink"      "candy_dessert_sweets" "other"               
[34] "no_use"               "brand_ecig"          

$nyts2019
 [1] "psu"                  "finwgt"               "stratum"             
 [4] "Age"                  "Sex"                  "Grade"               
 [7] "ECIGT"                "ECIGAR"               "ESLT"                
[10] "EELCIGT"              "EHOOKAH"              "EROLLCIGTS"          
[13] "EPIPE"                "ESNUS"                "EDISSOLV"            
[16] "EBIDIS"               "EHTP"                 "CCIGT"               
[19] "CCIGAR"               "CSLT"                 "CELCIGT"             
[22] "CHOOKAH"              "CROLLCIGTS"           "CPIPE"               
[25] "CSNUS"                "CDISSOLV"             "CBIDIS"              
[28] "CHTP"                 "brand_ecig"           "menthol"             
[31] "clove_spice"          "fruit"                "chocolate"           
[34] "alcoholic_drink"      "candy_dessert_sweets" "other"               
[37] "no_use"

It’s looking better! There are still some differences in the set of variables in the different years, but things are much closer.

Updating Values

Now that we have made some progress on the selection and names of the variables themselves, we will work on the values contained in the different variables.

We can start with updating the values for Age and Grade, so that they are more understandable.

Recall from the codebook for this year’s data set that Age isn’t listed in the way one might expect, i.e., it is not just a number of years, but a numerically valued categorical variable.

The same is true for Grade:

This is why it is so important to always check the codebook!!

We also want to replace the value of 19 for Age to be ">18" and the value of 13 for Grade to be replaced with "Ungraded/Other" Also according to the codebooks, numeric values of 1 indicate a survey answer of FALSE, while a value of 2 indicates TRUE. Sex needs to be recoded, but it is a bit unclear if it is encoded slightly differently across years.

Let’s create a function to make all these updates except for Sex. We will use the mutate function again, as well as mutate_all and recode to replace specific values of certain variables.

Now let’s update the Sex encoding:

It’s a bit difficult to tell if the encoding was the same across years, so let’s check using the count() function of the dplyr package.

According to the codebook, we should have:
1) 8,958 males in 2015 2) 10,438 males in 2016 3) 8,881 males in 2017
4) 10,069 males in 2018
5) 9,803 males in 2019

$nyts2015
# A tibble: 3 x 2
    Sex     n
  <dbl> <int>
1     1  8958
2     2  8622
3    NA   131

$nyts2016
# A tibble: 3 x 2
    Sex     n
  <dbl> <int>
1     1 10438
2     2 10082
3    NA   155

$nyts2017
# A tibble: 3 x 2
    Sex     n
  <dbl> <int>
1     1  8881
2     2  8815
3    NA   176

$nyts2018
# A tibble: 3 x 2
    Sex     n
  <dbl> <int>
1     1 10069
2     2  9920
3    NA   200

$nyts2019
# A tibble: 3 x 2
  Sex       n
  <chr> <int>
1 .N      116
2 1      9803
3 2      9099

Thus, it looks like males were encoded as 1 for each year.

  1. 8,958 males in 2015 where 1 = male
  2. 10,438 males in 2016 where 1 = male
  3. 8,881 males in 2017 where 1 = male
  4. 10,069 males in 2018 where 1 = male
  5. 9,803 males in 2019 where 1 = male
$nyts2015
# A tibble: 3 x 2
  Sex        n
  <fct>  <int>
1 male    8958
2 female  8622
3 <NA>     131

$nyts2016
# A tibble: 3 x 2
  Sex        n
  <fct>  <int>
1 male   10438
2 female 10082
3 <NA>     155

$nyts2017
# A tibble: 3 x 2
  Sex        n
  <fct>  <int>
1 male    8881
2 female  8815
3 <NA>     176

$nyts2018
# A tibble: 3 x 2
  Sex        n
  <fct>  <int>
1 male   10069
2 female  9920
3 <NA>     200

$nyts2019
# A tibble: 3 x 2
  Sex        n
  <fct>  <int>
1 .N       116
2 male    9803
3 female  9099

Looks good!

The years (2016-2019) that have flavors also need the flavor data to be logical (meaning TRUE or FALSE):

Now there are just a few changes needed that are specific to 2019:

Great! Now we have all the same variables and our values don’t need to be handled any differently for any of the years. Thus we can combine the data across years.

Rows: 95,465
Columns: 41
$ year                 <chr> "nyts2015", "nyts2015", "nyts2015", "nyts2015", …
$ psu                  <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt               <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum              <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age                  <fct> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, …
$ Sex                  <fct> female, male, male, male, female, female, male, …
$ Grade                <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
$ ECIGT                <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ ECIGAR               <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FA…
$ ESLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EELCIGT              <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ EROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, F…
$ EBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EFLAVCIGAR           <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ EHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ESNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CCIGT                <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CCIGAR               <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CSLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CELCIGT              <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ CROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CSNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ brand_ecig           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ EHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ CHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

We will want to do some of our analysis split by year, so we would like to be sure we have one variable that has the correct value for year. It looks like we just need to remove "nyts" from the year variable that we created from the names of the tibbles in our list and we should be all set.

Here is our clean and wrangled data:

.{scrollable}

Rows: 95,465
Columns: 41
$ year                 <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, …
$ psu                  <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt               <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum              <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age                  <fct> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, …
$ Sex                  <fct> female, male, male, male, female, female, male, …
$ Grade                <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
$ ECIGT                <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ ECIGAR               <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FA…
$ ESLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EELCIGT              <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ EROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, F…
$ EBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EFLAVCIGAR           <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ EHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ESNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CCIGT                <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CCIGAR               <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CSLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CELCIGT              <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ CROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CSNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ brand_ecig           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ EHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ CHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

Note that there are several variables where there are similar names, but with a C compared to an E in the variable name. Those starting with C are related to questions about current usage (last 30 days), while those with an E are related to usage across the subjects whole life (“ever” usage).

Reminder: Current users are a subset of ever users.

Variable Table

Click here to see a table about the variables

value 1 = yes, value 2 = no

Variable Details
year the year that the survey results from a particular student were acquired
psu the primary sampling unit for the survey weighting
finwgt the final analysis weight for the survey weighting
stratum the stratum used for variance estimation for the survey weighting
Age the age of the student when they took the survey
Sex the sex of the student when they took the survey
Grade the grade of the the student when the took the survey
ECIGT student reported having ever tried cigarette smoking, even one or two puffs
ECIGAR student reported having ever tried cigar, cigarillo, or little cigar smoking, even one or two puffs
ESLT student reported having ever tried chewing tobacco, snuff, or dip
EELCIGT student reported having ever tried e-cigarettes
EROLLCIGTS student reported having ever tried roll-your-own cigarettes
EFLAVCIGTS (2015 only) based on answer to “Which of the following tobacco products that you used in the past 30 days were flavored?”
EBIDIS student reported having ever tried bidis (small brown cigarettes wrapped in a leaf)
EFLAVCIGAR student reported having ever tried a flavored cigar (2015-2016)
EHOOKAH student reported having ever smoked tobacco from a hookah or a waterpipe
EPIPE student reported having ever smoked tobacco from a pipe (not hookah)
ESNUS student reported having ever used snus, such as Camel or Malboro Snus
EDISSOLV student reported having ever tried dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips
CCIGT student reported they smoked cigarettes on >= 1 of the past 30 days
CCIGAR student reported they smoked cigars on >= 1 of the past 30 days
CSLT student reported they used chewing tobacco, snuff, or dip on >= 1 of the past 30 days
CELCIGT student reported they used electronic cigarettes or e-cigarettes one or more days in the past 30
CROLLCIGTS student reported they smoked roll-your-own cigarettes during the past 30 days
CFLAVCIGTS (2015 only) based on answer to “Which of the following tobacco products that you used in the past 30 days were flavored?”
CBIDIS student reported they smoked bidis during the past 30 days
CHOOKAH student reported they smoked tobacco in a hookah on >= 1 of the past 30 days
CPIPE student reported they smoked tobacco in a pipe (not hookah) during the past 30 days
CSNUS student reported they used snus during the past 30 days
CDISSOLV student reported they used dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips during the past 30 days
brand_ecig student answer to “During the past 30 days, what brand of e-cigarettes did you usually use?”
menthol student selected Menthol or mint as the answer to “What flavors of tobacco products have you used in the past 30 days? (select one or more)”
clove_spice student selected clove or spice as the answer to “What flavors of tobacco products have you used in the past 30 days? (select one or more)”
fruit student selected fruit as the answer to “What flavors of tobacco products have you used in the past 30 days? (select one or more)”
chocolate student selected chocolate as the answer to “What flavors of tobacco products have you used in the past 30 days? (select one or more)”
alcoholic_drink student selected alcoholic drink (such as wine, cognac, margarita, or other cocktails) as the answer to “What flavors of tobacco products have you used in the past 30 days? (choose one or more)”
candy_dessert_sweets student selected candy, desserts or other sweets as the answer to “What flavors of tobacco products have you used in the past 30 days? (choose one or more)”
other student selected some other flavor not listed as the answer to “What flavors of tobacco products have you used in the past 30 days? (choose one or more)”
no_use student reported that they did not use any flavored tobacco products in the past 30 days or did not answer the question about using flavors
EHTP student reported having ever tried heated (also known as “heat-not-burn”) tobacco products
CHTP student reported they used heated tobacco products during the past 30 days

Data Visualization

Recall that our main questions were:

  1. How has tobacco/nicotine and e-cigarette use by American youths changed since 2015?
  2. How do vaping rates compare between males and females?

  3. What vaping brands and flavors appear to be used the most frequently?
    We will base this on the following survey questions:
    > “During the past 30 days, what brand of e-cigarettes did you usually use?”
    >" What flavors of tobacco products have you used in the past 30 days?"

  4. Have vaping rates possibly influenced tobacco/nicotine use?

We are now going to create visualizations to explore each of these questions.

For many of these questions we will be interested in both current and ever users:

We define these two groups based on the survey questions:

  1. current = students who used the product for >=1 day in the past 30 days
  2. ever = students who report having used or tried a product at any point in time

All current users are therefore ever users but not all ever users are current users. Thus, current users are a subset of ever users.

To add these groups to our data we will do a bit more wrangling. Using the mutate() function again of the dplyr package, we will first create two new variables that are the sum of the values for questions that relate to the use of any tobacco product surveyed about ever (questions that start with the letter “E”), and questions that relate to the current use of any tobacco products surveyed about (questions that start with the letter “C”).

Here are some examples for these data entries:

  • EPIPE: Students who reported they have smoked tobacco from a pipe (not hookah).
  • CPIPE: Students who reported they smoked tobacco in a pipe (not hookah) during the past 30 days.
  • EROLLCIGTS: RECODE: Students who reported they have tried smoking roll-your-own cigarettes.
  • CROLLCIGTS: RECODE: Students who reported smoked roll-your-own cigarettes during the past 30 days.

Based on many questions like this:

In the past 30 days, which of the following products have you used on at least one day? (Select one or more) A. Roll-your-own cigarettes
B. Pipes filled with tobacco (not hookah or waterpipe)
C. Snus, such as Camel, Marlboro, or General Snus
D. Dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips
E. Bidis (small brown cigarettes wrapped in a leaf)
F. I have not used any of the products listed above in the past 30 days

Which of the following tobacco products have you ever tried, even just one time? (Select one or more)
A. Roll-your-own cigarettes
B. Pipes filled with tobacco (not hookah or waterpipe)
C. Snus, such as Camel, Marlboro, or General Snus
D. Dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips
E. Bidis (small brown cigarettes wrapped in a leaf)
F. I have never tried any of the products listed above

We will sum across the variables that relate to ever or current tobacco usage questions to get determine if the student answered yes to any of the ever or current questions.

We will then use the case_when() function of the dplyr package to convert the sum values to TRUE or FALSE based on the threshold of zero. If the sum is greater than zero, then we know the student answered yes to at least one question.

Rows: 95,465
Columns: 45
$ year                 <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, …
$ psu                  <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt               <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum              <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age                  <fct> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, …
$ Sex                  <fct> female, male, male, male, female, female, male, …
$ Grade                <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
$ ECIGT                <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ ECIGAR               <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FA…
$ ESLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EELCIGT              <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ EROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, F…
$ EBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EFLAVCIGAR           <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ EHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ESNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CCIGT                <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CCIGAR               <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CSLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CELCIGT              <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ CROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CSNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ brand_ecig           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ EHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ CHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tobacco_sum_ever     <int> 1, 4, 0, 3, 0, 2, 8, 4, 0, 0, 0, 1, 1, 0, 0, 4, …
$ tobacco_sum_current  <int> 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ tobacco_ever         <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE…
$ tobacco_current      <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, F…

Rows: 95,465
Columns: 53
$ year                 <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, …
$ psu                  <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt               <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum              <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age                  <fct> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, …
$ Sex                  <fct> female, male, male, male, female, female, male, …
$ Grade                <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
$ ECIGT                <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ ECIGAR               <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FA…
$ ESLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EELCIGT              <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ EROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, F…
$ EBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EFLAVCIGAR           <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ EHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ EPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ESNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CCIGT                <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CCIGAR               <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ CSLT                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CELCIGT              <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ CROLLCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CFLAVCIGTS           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CBIDIS               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CHOOKAH              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CPIPE                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CSNUS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CDISSOLV             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ brand_ecig           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ EHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ CHTP                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tobacco_sum_ever     <int> 1, 4, 0, 3, 0, 2, 8, 4, 0, 0, 0, 1, 1, 0, 0, 4, …
$ tobacco_sum_current  <int> 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ tobacco_ever         <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE…
$ tobacco_current      <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, F…
$ ecig_sum_ever        <int> 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, …
$ ecig_sum_current     <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ non_ecig_sum_ever    <int> 1, 3, 0, 2, 0, 1, 7, 3, 0, 0, 0, 0, 1, 0, 0, 3, …
$ non_ecig_sum_current <int> 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ecig_ever            <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ ecig_current         <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ non_ecig_ever        <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE…
$ non_ecig_current     <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, …

Finally, we are also interested in students that only use e-cigarettes and those that only other forms of tobacco. We will again use the case_when function to accomplish creating new variables.

Here is a toy example of what we will do with the larger dataset:

Recall that current users are a subset of ever users, thus students would typically answer yes to having tried vaping products if they had used them one or more days in the past 30 days.

First we will make a small toy dataset:

# A tibble: 9 x 4
  ecig_ever non_ecig_ever ecig_current non_ecig_current
  <chr>     <chr>         <chr>        <chr>           
1 TRUE      TRUE          TRUE         TRUE            
2 TRUE      FALSE         FALSE        FALSE           
3 TRUE      FALSE         FALSE        TRUE            
4 TRUE      FALSE         TRUE         FALSE           
5 FALSE     FALSE         TRUE         TRUE            
6 FALSE     FALSE         FALSE        FALSE           
7 TRUE      TRUE          FALSE        FALSE           
8 FALSE     TRUE          FALSE        FALSE           
9 FALSE     TRUE          FALSE        TRUE

Now, let’s look at identifying sutents who have only ever tried e-cigarettes, but are not current users, nor have they tried or currently use other tobacco products.

# A tibble: 9 x 5
  ecig_ever non_ecig_ever ecig_current non_ecig_current ecig_only_ever
  <chr>     <chr>         <chr>        <chr>            <lgl>         
1 TRUE      TRUE          TRUE         TRUE             FALSE         
2 TRUE      FALSE         FALSE        FALSE            TRUE          
3 TRUE      FALSE         FALSE        TRUE             FALSE         
4 TRUE      FALSE         TRUE         FALSE            FALSE         
5 FALSE     FALSE         TRUE         TRUE             FALSE         
6 FALSE     FALSE         FALSE        FALSE            FALSE         
7 TRUE      TRUE          FALSE        FALSE            FALSE         
8 FALSE     TRUE          FALSE        FALSE            FALSE         
9 FALSE     TRUE          FALSE        TRUE             FALSE

We can see from the fourth row, that even though the student reported yes to ever trying e-cigarettes, they also reported yes to currently using e-cigarettes, thus the value for only ever trying e-cigarettes is FALSE. Additionally we can see from the seventh row that similarly even though the student reported yest to ever trying e0cigarettes, they also reported yes to ever trying other products, and the value for only ever trying e-cigarettes is FALSE. Importantly, we can see from the 6th row, that if all responses are negative than the value is FALSE.

Now we will expand this to the other possible categories. Notice since, current users are a subset of ever users, it doesn’t matter if a user reports yes to ever trying e-cigarettes, they can still be a current user.

# A tibble: 9 x 9
  ecig_ever non_ecig_ever ecig_current non_ecig_current ecig_only_ever
  <chr>     <chr>         <chr>        <chr>            <lgl>         
1 TRUE      TRUE          TRUE         TRUE             FALSE         
2 TRUE      FALSE         FALSE        FALSE            TRUE          
3 TRUE      FALSE         FALSE        TRUE             FALSE         
4 TRUE      FALSE         TRUE         FALSE            FALSE         
5 FALSE     FALSE         TRUE         TRUE             FALSE         
6 FALSE     FALSE         FALSE        FALSE            FALSE         
7 TRUE      TRUE          FALSE        FALSE            FALSE         
8 FALSE     TRUE          FALSE        FALSE            FALSE         
9 FALSE     TRUE          FALSE        TRUE             FALSE         
# … with 4 more variables: ecig_only_current <lgl>, non_ecig_only_ever <lgl>,
#   non_ecig_only_current <lgl>, no_use <lgl>

Take a minute to check that the values are what we would expect.

Ok, now we are going to make a Group variable based on the new variables we just made. If a student has only ever tried or currently uses e-cigarettes, the value will be Only e-cigarettes. The same will be true for other tobacco products. If a student has tried either they will be called Both.

# A tibble: 9 x 10
  ecig_ever non_ecig_ever ecig_current non_ecig_current ecig_only_ever
  <chr>     <chr>         <chr>        <chr>            <lgl>         
1 TRUE      TRUE          TRUE         TRUE             FALSE         
2 TRUE      FALSE         FALSE        FALSE            TRUE          
3 TRUE      FALSE         FALSE        TRUE             FALSE         
4 TRUE      FALSE         TRUE         FALSE            FALSE         
5 FALSE     FALSE         TRUE         TRUE             FALSE         
6 FALSE     FALSE         FALSE        FALSE            FALSE         
7 TRUE      TRUE          FALSE        FALSE            FALSE         
8 FALSE     TRUE          FALSE        FALSE            FALSE         
9 FALSE     TRUE          FALSE        TRUE             FALSE         
# … with 5 more variables: ecig_only_current <lgl>, non_ecig_only_ever <lgl>,
#   non_ecig_only_current <lgl>, no_use <lgl>, Group <chr>

Rows: 95,465
Columns: 58
$ year                  <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
$ psu                   <chr> "015438", "015438", "015438", "015438", "015438…
$ finwgt                <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.874…
$ stratum               <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3"…
$ Age                   <fct> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,…
$ Sex                   <fct> female, male, male, male, female, female, male,…
$ Grade                 <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,…
$ ECIGT                 <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TR…
$ ECIGAR                <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ ESLT                  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,…
$ EELCIGT               <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TR…
$ EROLLCIGTS            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,…
$ EFLAVCIGTS            <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, …
$ EBIDIS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ EFLAVCIGAR            <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, …
$ EHOOKAH               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ EPIPE                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ ESNUS                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,…
$ EDISSOLV              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CCIGT                 <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CCIGAR                <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ CSLT                  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CELCIGT               <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,…
$ CROLLCIGTS            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CFLAVCIGTS            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CBIDIS                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CHOOKAH               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CPIPE                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CSNUS                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ CDISSOLV              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ brand_ecig            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ menthol               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ clove_spice           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ fruit                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ chocolate             <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ alcoholic_drink       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ candy_dessert_sweets  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ other                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ no_use                <lgl> FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, …
$ EHTP                  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ CHTP                  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tobacco_sum_ever      <int> 1, 4, 0, 3, 0, 2, 8, 4, 0, 0, 0, 1, 1, 0, 0, 4,…
$ tobacco_sum_current   <int> 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ tobacco_ever          <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ tobacco_current       <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ ecig_sum_ever         <int> 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,…
$ ecig_sum_current      <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ non_ecig_sum_ever     <int> 1, 3, 0, 2, 0, 1, 7, 3, 0, 0, 0, 0, 1, 0, 0, 3,…
$ non_ecig_sum_current  <int> 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ ecig_ever             <lgl> FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TR…
$ ecig_current          <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,…
$ non_ecig_ever         <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRU…
$ non_ecig_current      <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ecig_only_ever        <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ ecig_only_current     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ non_ecig_only_ever    <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ non_ecig_only_current <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
$ Group                 <chr> "Only other products", "Both", "Neither", "Both…

Question 1

We are interested in investing how vaping product use has compared with other tobacco use. First we want to get a sense of how tobacco use has changed in general since 2015.

To create a visualiztion about how tobacco usage has changed over time, we will first convert the usage data to a mean value for each year. To do this we will use the group_by() and summarize function of the dplyr package. This will create new variables which we will name Ever and Current based on the mean of the percentages of tobacco_ever and tobacco_current for each year.

# A tibble: 5 x 3
   year  Ever Current
  <dbl> <dbl>   <dbl>
1  2015  36.8    18.0
2  2016  33.4    14.0
3  2017  31.8    14.4
4  2018  34.7    18.7
5  2019  39.7    22.4

We will use the pivot_longer function to take all collumns except year (in this case the Ever and Current columns), to create a column called User that will contain the current column name information and a column called Percentage of students which will contain the mean percentage values that we just calculated. This converts our data into a format called long format.

# A tibble: 10 x 3
    year User    `Percentage of students`
   <dbl> <chr>                      <dbl>
 1  2015 Ever                        36.8
 2  2015 Current                     18.0
 3  2016 Ever                        33.4
 4  2016 Current                     14.0
 5  2017 Ever                        31.8
 6  2017 Current                     14.4
 7  2018 Ever                        34.7
 8  2018 Current                     18.7
 9  2019 Ever                        39.7
10  2019 Current                     22.4

You may have noticed that our data is longer than it used to be! Hence the name of the function pivot_longer(). Data is often easier to plot when it is in this format.

Now we will use this data to create a plot using the ggplot2 package.

The first thing we do to create a plot is specify what data we are using for our x axis and y axis with theaes() argument of the ggplot() function. Then we add layers to our plot that specify what type of plot we would like to create. We can use the geom_line() function to create lines for each type of user. We specify that we want to use different line types for each user using aes(). We will also add points to our lines using the geom_point() function. We can add additional layers to specify colors and details about labels and legends etc.

Nice! Now we can see how overall tobacco usage has changed since 2017. It appears that usage has increased a bit since 2017, and has surpassed the levels in 2015.

What about e-cigarette use? How has the usage of e-cigarettes changed over time?

It looks like the shape of the plot is very similar to tobacco usage overall. We see a downward trend until 2017 when the rate of both current and ever users increased. Recall that this is in agreement with the articles that we referenced earlier. We can see that the slope looks steeper for e-cigarette usage as compared to all tobacco products (including e-cigarettes).

Now let’s plot e-cigarette usage and tobacco products usage (besides e-cigarettes) together on the same plot. In this case we will look at students who have only smoked either of e-cigarettes or only other tobacco products besides e-cigarettes.

In this case we will have four groups (only e-cigarette ever users, only e-cigarette current users, only non-e-cigarette ever tobbaco users, and only non-e-cigarette current tobacco users) to plot, therefore, it would be useful to add color to our plot.

One important thing to keep in mind when creating plots, is that individuals with color blindness may have a difficult time distinguishing groups when certian color chocies are used.

One great option is to use the viridis package, which offers color palattes with colors that are still distinguishable by individuals with most forms of color blindness.

We can choose which colors we want to use by using the show_col() function of the scales package.

Here are some color options:

We will select the first and fourth colors for our plot. To add these specific colors we will use the scale_color_manual() function of the ggplot2 package.

We will calculate the mean ever and current usage percentages for students who had only used e-cigarette or other tobacco products (besides e-cigarettes) for each year again using the group_by() function and the summarize() function. We will again use the pivot_longer function to convert our data to long format. We will also use the separate() function of the tidyr package to create two variables from one of variables. This is done by separating by, in this case, a space.

# A tibble: 6 x 4
   year User    Product   `Percentage of students`
  <dbl> <chr>   <chr>                        <dbl>
1  2015 Ever    e-cig                         4.36
2  2015 Current e-cig                         1.54
3  2015 Ever    non-e-cig                     7.06
4  2015 Current non-e-cig                     3.35
5  2016 Ever    e-cig                         4.54
6  2016 Current e-cig                         1.23

We can see from this plot that the percentage of students who had only ever tried (or currently used) e-cigarettes was greatly increasing since 2017, while in contrast the percentage of students who had only ever tried non-e-cigarette tobacco products was diminishing over time. In fact, we can see that in 2019 the percentage of students who were current e-cigarette users surpassed the percentage that had tried a non-e-cigarette product even just once.

We will explore the relaitonship between e-cigarette usage and other tobacco products a bit later in the case study.

Recall that we made a variable called Group that identified students who used either just e-cigarette products, just other tobacco products (besides e-cigarettes), or students who used both e-cigarettes and some other type of tobacco product.

# A tibble: 4 x 2
  Group                   n
  <chr>               <int>
1 Both                16517
2 Neither             61738
3 Only e-cigarettes    7866
4 Only other products  9344

We will now make a plot over time of each of these groups.

# A tibble: 6 x 3
  Group    year     n
  <chr>   <dbl> <int>
1 Both     2015  3634
2 Both     2016  3297
3 Both     2017  2623
4 Both     2018  3321
5 Both     2019  3642
6 Neither  2015 11188

Question 2

Now we want to look how e-cigarette smoking rates compare between males and females across time.

We will calculate the mean e-cigarette ever and current percentages for each year and sex category again using the group_by() function and the summarize() function. Notice, that in this case we are grouping by two variables by simply using group_by(year, Sex). We will again use the pivot_longer function to convert our data to long format.

# A tibble: 6 x 4
# Groups:   year [1]
   year Sex    User    `Percentage of students`
  <dbl> <fct>  <chr>                      <dbl>
1  2015 male   Ever                       29.3 
2  2015 male   Current                    13.3 
3  2015 female Ever                       24.3 
4  2015 female Current                     9.05
5  2015 <NA>   Ever                       25   
6  2015 <NA>   Current                    11.8

It looks like the rates are fairly similar between the sexes, however the rate for males appears to be consistently higher across time.

Question 3

We are also interested in what vaping brands and flavors appear to be used the most frequently. Only the 2019 data has this information. Therefore, we will filter for just this year using the filter() function of the dplyr package. We will use the summarize() function slightly differently this time, to calculate the total number of students using each brand n using the n() function and the sum() function to calculate the percent for each brand based on the counts.

# A tibble: 7 x 4
  brand_ecig     n total Percent
  <fct>      <int> <int>   <dbl>
1 Blu          111  3604   3.08 
2 JUUL        2028  3604  56.3  
3 Logic         36  3604   0.999
4 MarkTen       32  3604   0.888
5 NJOY          44  3604   1.22 
6 Other       1253  3604  34.8  
7 Vuse         100  3604   2.77

Juul appears to be the most widely used brand. This is in agreement with a recent article:

Huang J, Duan Z, Kwok J, et al. Tob Control 2019;28:146–151.

Huang J, Duan Z, Kwok J, et al. Tob Control 2019;28:146–151.

We are also interested in how the usage of different flavors has changed over the years. To evaluate this we will calculate the precentage of students using each flavor each year - this includes usage of any type of flavored tobacco product.

From this plot, we can see that fruit flavors are the most widely used products, followed by menthol or mint flavored products.

We will now look specificially at the usage of flavored e-cigarette products vs other flavored tobacco products.

Recall that we made a variable called Group that identified students who used either just e-cigarette products, just other tobacco products (besides e-cigarettes), or students who used both e-cigarettes and some other type of tobacco product.

# A tibble: 4 x 2
  Group                   n
  <chr>               <int>
1 Both                16517
2 Neither             61738
3 Only e-cigarettes    7866
4 Only other products  9344

The majority of the students that use tobacco use both e-cigarettes and other products.

plot5 <- nyts_data %>%
  filter(year != 2015) %>%
  # filter(menthol == TRUE|
  #          clove_spice == TRUE|
  #          fruit == TRUE|
  #          chocolate == TRUE|
  #          alcoholic_drink == TRUE|
  #          candy_dessert_sweets == TRUE|
  #          other == TRUE) %>%
# we want to remove students that used both e-cigarette and other tobacco products
  filter(Group != "Both") %>%
  group_by(year, Group) %>%
summarize(Menthol=(mean(menthol, na.rm = TRUE)*100),
         `Clove or Spice`=(mean(clove_spice, na.rm = TRUE)*100),
          Fruit=(mean(fruit, na.rm = TRUE)*100),
          Chocolate=(mean(chocolate, na.rm = TRUE)*100),
          `Alcoholic Drink`=(mean(alcoholic_drink, na.rm = TRUE)*100),
          `Candy/Desserts/Sweets`=(mean(candy_dessert_sweets, na.rm = TRUE)*100),
          Other = (mean(other, na.rm = TRUE)*100),
          Respondents=n()) %>%
  #converting all columns between and including Menthol and Other to one column called Flavor
  pivot_longer(cols = Menthol:Other, names_to = "Flavor", values_to = "Percentage of students") %>%
  filter(!is.na(`Percentage of students`),
         Flavor!="Other") %>%
  group_by(Flavor) %>%
  mutate(affirmative=(Respondents * `Percentage of students`)/100) %>%
  mutate(flavor_mean = sum(affirmative)/sum(Respondents)) %>%
  ungroup() %>%
  mutate(flavor_mean_rank = dense_rank(flavor_mean),
         Flavor = fct_reorder(Flavor, flavor_mean_rank)) %>%
  ggplot(aes(x=year, y=`Percentage of students`, color=Group)) +
  facet_wrap(.~Flavor,ncol=3) +
  geom_line() + 
  geom_point(show.legend = FALSE) + 
  theme_minimal() +
  theme(legend.position = "bottom",
          axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 90)) + 
  labs(title = "Among users of only one type of product, what vaping flavors appear to be used the most frequently?",
       subtitle = "Percent reporting only e-cigarette use vs only other nicotine product use")

plot5

Question 4

Have vaping rates possibly influenced tobacco/nicotine use?

Weighted Sample

plotA_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
summarize(tobacco_ever_year = survey_mean(tobacco_ever, vartype = "ci", na.rm=TRUE),
            tobacco_current_year = survey_mean(tobacco_current, vartype = "ci", na.rm=TRUE))  %>%
  mutate_at(vars(-year), "*", 100) %>%
    pivot_longer(cols = -year, names_to = "Type", values_to = "Percentage of students") %>%
  mutate(Estimate = case_when(grepl("_low", Type) ~ "Lower",
                          grepl("_upp", Type) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Type) ~ "Ever",
                          grepl("current", Type) ~ "Current",
                          TRUE ~ "Mean")) %>%
  dplyr::select(-Type) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  ggplot(aes(x=year,y=Mean)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
    scale_y_continuous(breaks = seq(0,70,by=10),
                       labels = seq(0,70,by=10),
                       limits = c(0,70)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "Nicotine product users more prevalent after 2017",
         y = "% of students")

plotB_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
  summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
  mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("current", Category) ~ "Current",
                          TRUE ~ "Ever",),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  filter(User=="Ever") %>%
  dplyr::rename("Lower_temp" = Upper,
                "Upper_temp" = Lower) %>%
  dplyr::rename("Lower"=Lower_temp,
                "Upper"=Upper_temp) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(linetype=1) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_y_continuous(breaks = seq(10, 60, by = 10), limits = c(10,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% ever trying e-cigarettes increases &\n% ever trying other products decreases",
         y = "% of students")

#### the wrangling looks the same as the above plot...
plotC_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
    mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Category) ~ "Ever",
                          grepl("current", Category) ~ "Current"),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
  scale_y_continuous(breaks = seq(0, 60, by = 10), limits = c(0,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% Using e-cigarettes increases &\n% using Other products decreases",
         y = "% of students")

title_w <- ggdraw() + 
  draw_label(
    expression("Have vaping rates possibly influenced tobacco/nicotine use?"),
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_w <- plot_grid(plotA_w,
                     rel_widths = c(1),
                     align = "v",
                     axis = "bt")
plotsBC_w <- plot_grid(plotB_w,
                     plotC_w,
                     rel_widths = c(1,1),
                     align = "v",
                     axis = "bt")

legend_w <- get_legend(plot8 +
                       theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

figure_w <- plot_grid(title_w,
                      plotsA_w,
                      plotsBC_w,
                      legend_w,
                      ncol = 1,
                      rel_heights = c(0.1,
                                      1,
                                      1,
                                      0.1),
                      scale = 1.0)

figure_w

Hypothetical Cohort

plotA_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
summarize(tobacco_ever_year = survey_mean(tobacco_ever, vartype = "ci", na.rm=TRUE),
            tobacco_current_year = survey_mean(tobacco_current, vartype = "ci", na.rm=TRUE))  %>%
  mutate_at(vars(-year), "*", 100) %>%
    pivot_longer(cols = -year, names_to = "Type", values_to = "Percentage of students")%>%
  mutate(Estimate = case_when(grepl("_low", Type) ~ "Lower",
                          grepl("_upp", Type) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Type) ~ "Ever",
                          grepl("current", Type) ~ "Current",
                          TRUE ~ "Mean")) %>%
  dplyr::select(-Type) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  #spread(Estimate, `Percentage of students`) %>%
  ggplot(aes(x=year,y=Mean)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_linetype_manual(values = c(2,1)) +
    scale_y_continuous(breaks = seq(0,70,by=10),
                       labels = seq(0,70,by=10),
                       limits = c(0,70)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "Nicotine product users becoming increasingly prevalent",
         y = "% of students")

plotB_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
  summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
   pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students")%>%
  mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("current", Category) ~ "Current",
                          TRUE ~ "Ever",),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  filter(User=="Ever") %>%
  dplyr::rename("Lower_temp" = Upper,
                "Upper_temp" = Lower) %>%
  dplyr::rename("Lower"=Lower_temp,
                "Upper"=Upper_temp) %>%
  ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(linetype=1) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_y_continuous(breaks = seq(10, 60, by = 10), limits = c(10,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% ever trying nicotine products increases",
         y = "% of students")

plotC_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students")%>%
    mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Category) ~ "Ever",
                          grepl("current", Category) ~ "Current"),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_linetype_manual(values = c(2,1)) +
  scale_y_continuous(breaks = seq(0, 60, by = 10), limits = c(0,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "E-cigarette use surpasses use of other nicotine products",
         y = "% of students")

title_w_8 <- ggdraw() + 
  draw_label(
    expression("Among"~8^th~"graders in 2015, have vaping rates possibly influenced tobacco/nicotine use?"),
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_w_8 <- plot_grid(plotA_w_8,
                        rel_widths = c(1),
                        align = "v",
                        axis = "bt")

plotsBC_w_8 <- plot_grid(plotB_w_8,
                         plotC_w_8,
                         rel_widths = c(1,1),
                         axis = "bt")

legend_w_8 <- get_legend(plot8 +
                       theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

figure_w_8 <- plot_grid(title_w_8,
                        plotsA_w_8,
                        plotsBC_w_8,
                        legend_w_8,
                        ncol = 1,
                        rel_heights = c(0.1,
                                      1,
                                      1,
                                      0.1),
                        scale = 1.0
)

figure_w_8

Final Figure

title_final <- ggdraw() +
  draw_label(
    expression("What is the relationship between vaping rates and tobacco/nicotine use?"),
    fontface = 'bold',
    size=16,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_uw_final <- ggdraw() + 
  draw_label(
    expression(~6^th~"-"~12^th~"graders, unweighted"),
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_w_final <- ggdraw() + 
  draw_label(
    expression(~6^th~"-"~12^th~"graders, weighted"),
    fontface = 'bold',
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_w_8_final <- ggdraw() + 
  draw_label(
    expression(~8^th~"graders in 2015, weighted"),
    fontface = 'bold',
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_final <- plot_grid(subtitle_uw_final,
                            subtitle_w_final,
                            subtitle_w_8_final,
                            ncol = 3)

plotsA_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of e-cigarette use by user type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_final <- plot_grid(plotA_uw + theme(plot.title = element_blank()),
                          plotA_w + theme(plot.title = element_blank()),
                          plotA_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

plotsB_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of ever use by product type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsB_final <- plot_grid(plotB_uw + theme(plot.title = element_blank()),
                          plotB_w + theme(plot.title = element_blank()),
                          plotB_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

plotsC_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of nicotine product use by product & user type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsC_final <- plot_grid(plotC_uw + theme(plot.title = element_blank()),
                          plotC_w + theme(plot.title = element_blank()),
                          plotC_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

legend_final <- get_legend(plot8 +
                             theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

final_plot <- plot_grid(title_final,
          plotsA_title_final,
          subtitle_final,
          plotsA_final,
          plotsB_title_final,
          subtitle_final,
          plotsB_final,
          plotsC_title_final,
          subtitle_final,
          plotsC_final,
          legend_final,
          ncol = 1,
          rel_heights = c(0.2,
                          0.2,
                          0.1,
                          1,
                          0.2,
                          0.1,
                          1,
                          0.2,
                          0.1,
                          1,
                          0.1))

final_plot

Suggested Homework

  • Apply survey weights to one of the figures produced in this case study in which weighted estimates were not produced. Include error bars in the updated figure.
    • Does the figure change after the application of survey weights?
    • If so, describe how.
  • Reproduce final_plot above for a different cohort of your choice.

Notes

Ever and current variables are limited to those shared by all years of data included in this case study.

New code: Knit time: 36.561 secs. Previous code: ~ 3 m.

Problems

I had difficulty producing a plot that succinctly presented a trend. It’s very easy to produce plots that are very useful once one is familiar with the data. Some plots, however, cannot stand alone and need additional context to be clear for those without prior knowledge of the data. When I first shared a plot I had been working on with others, it became clear that in my effort to present a complicated idea briefly I had left out information that would make the trend easily interpretable. To solve this issue, I began to present visualizations of the data alongside my original plot. The final figure I created contained several additional plots, each presenting the same trend at a different level than my initial plot.

My “centerpiece” plot is the middle plot in final_plot. The 8 plots around it help provide a very clear picture of what is going on in the US with regards to e-cigarette use and nicotine product use at large. On its own, it’s difficult to understand the trends in the US and how important the weighting scheme is for inference. Once you add the left and right columns, it’s clear what is going on.

Data Analysis

content header

Summary

cool visualization that summarizes everything… could add this somewhere: https://www.fda.gov/tobacco-products/youth-and-tobacco/youth-tobacco-use-results-national-youth-tobacco-survey

Session info

R version 4.0.1 (2020-06-06)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_1.0.0     srvyr_0.3.10      forcats_0.5.0     viridis_0.5.1    
 [5] viridisLite_0.3.0 scales_1.1.1      ggplot2_3.3.1     tidyr_1.1.0      
 [9] readr_1.3.1       dplyr_1.0.0       purrr_0.3.4       stringr_1.4.0    
[13] readxl_1.3.1      knitr_1.28        here_0.1         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6     highr_0.8        cellranger_1.1.0 pillar_1.4.4    
 [5] compiler_4.0.1   base64enc_0.1-3  tools_4.0.1      digest_0.6.25   
 [9] lattice_0.20-41  evaluate_0.14    lifecycle_0.2.0  tibble_3.0.1    
[13] gtable_0.3.0     pkgconfig_2.0.3  rlang_0.4.6      Matrix_1.2-18   
[17] cli_2.0.2        DBI_1.1.0        yaml_2.2.1       xfun_0.14       
[21] gridExtra_2.3    withr_2.2.0      mitools_2.4      generics_0.0.2  
[25] vctrs_0.3.1      hms_0.5.3        rprojroot_1.3-2  grid_4.0.1      
[29] tidyselect_1.1.0 glue_1.4.1       R6_2.4.1         fansi_0.4.1     
[33] survival_3.1-12  rmarkdown_2.2    farver_2.0.3     magrittr_1.5    
[37] splines_4.0.1    backports_1.1.7  ellipsis_0.3.1   htmltools_0.4.0 
[41] assertthat_0.2.1 colorspace_1.4-1 labeling_0.3     utf8_1.1.4      
[45] survey_4.0       stringi_1.4.6    munsell_0.5.0    crayon_1.3.4
---
title: "Open Case Studies : Vaping Behaviors in American Youth"
author: "Michael Ontiveros, Carrie Wright, PhD. "

css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes

---
<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/logo.jpg");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>

```{r, echo=FALSE}
knit_time_start <- Sys.time()
```

```{r, echo=FALSE}
knitr::opts_chunk$set(fig.width=10, fig.height=8, dpi=300) 
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)
```


#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "final_plot.png"))
```
####




## {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 

## **Motivation**
*** 
According to a recent [report](https://www.cdc.gov/mmwr/volumes/68/wr/mm6806e1.htm?s_cid=mm6806e1_w){target="_blank"}, overall tobacco use **increased** in youths (middle school and high school students) in the United States in 2017 and 2018, despite previous years of declining use.

This major increase is attributed to an increase in the use of electronic cigarette (e-cigarette) products.

E-cigarettes are referred to by many different names, including but not limited to:

1) Electronic nicotine delivery systems (ENDS)
2) Vapes
3) e-hookahs
4) vape pens
5) tanks
6) mods

The devices vary greatly:

```{r, echo = FALSE, fig.align ="center"}

include_graphics("https://www.lung.org/getmedia/8ac8ab8c-e7fc-497b-8384-441615f50ff0/ecigs_K.jpg.jpg")
```

##### [[source](https://www.lung.org/quit-smoking/e-cigarettes-vaping/lung-health)]

See this [CDC guide](https://www.cdc.gov/tobacco/basic_information/e-cigarettes/pdfs/ecigarette-or-vaping-products-visual-dictionary-508.pdf){target="_blank"} and the [American Lung Association website](https://www.lung.org/quit-smoking/e-cigarettes-vaping/lung-health){target="_blank"} for more information. 

The report found that:

> During 2017–2018, current use of any tobacco product **increased 38.3%** (from 19.6% to 27.1%) among high school students and **28.6%** (from 5.6% to 7.2%) among middle school students; e-cigarette use **increased 77.8%** (from 11.7% to 20.8%) among high school students and **48.5%** (from 3.3% to 4.9%) among middle school students.


In 2018, the [Federal Drug Administration (FDA) in the United States](https://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.31868){target="_blank"} stated that e-cigarette usage use among youth reached:  

> “nothing short of an **epidemic proportion of growth**”


In this case study, we will be investigating the same data used in the report that generated the above findings. This data comes from the [The National Youth Tobacco Survey (NYTS)](https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/index.htm){target="_blank"}.

#### {.reference_block}

Gentzke, Andrea S., Melisa Creamer, Karen A. Cullen, Bridget K. Ambrose, Gordon Willis, Ahmed Jamal, and Brian A. King.  “Vital Signs: Tobacco Product Use Among Middle and High School Students - United States, 2011-2018.” **MMWR. Morbidity and Mortality Weekly Report** 68 (6): 157–64 (2019).

####


## **Main Questions**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

1) How has tobacco/nicotine and e-cigarette use by American youths changed since 2015?
2) How do vaping rates compare between males and females?
3) What vaping brands and flavors appear to be used the most frequently?  
We will base this on the following survey questions:   
> "During the past 30 days, what brand of e-cigarettes did you usually use?"   
>" What flavors of tobacco products have you used in the past
30 days?" 

4) Have vaping rates possibly influenced tobacco/nicotine use?

####


## **Learning Objectives** 
*** 

In this case study, we will cover how to import data from multiple files efficiently, how to import data from excel files, and how to make a variety of visualizations to compare multiple groups across time. We will also demonstrate how to work with codebooks. We will cover the concept of survey weighting and introduce the `srvyr` package. We will discuss the difference between pooled cross-sectional data and panel data. We will especially focus on using packages and functions from the [`Tidyverse`](https://www.tidyverse.org/){target="_blank"} for wrangling data, such as `tidyr` and `dplyr` and for visualization, such as as `ggplot2`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.


```{r, out.width = "20%", echo = FALSE, fig.align ="center"}

include_graphics("https://tidyverse.tidyverse.org/logo.png")
```


*** 


We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(stringr)
library(purrr)
library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)
library(scales)
library(viridis)
library(forcats)
library(srvyr)
library(cowplot)
```

 Package   | Use                                                                         
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data  
[readxl](https://readxl.tidyverse.org/){target="_blank"}      | to import the data in the excel files 
[stringr](https://stringr.tidyverse.org/articles/stringr.html){target="_blank"}    | to manipulate the character strings within the data  
[purrr](https://purrr.tidyverse.org/){target="_blank"}   | to import the data in all the different excel and csv files efficiently
[dplyr](https://dplyr.tidyverse.org/){target="_blank"}      | to arrange/filter/select/compare specific subsets of the data  
[readr](https://readr.tidyverse.org/){target="_blank"}      | to import the CSV file data
[tidyr](https://tidyr.tidyverse.org/){target="_blank"}      | to rearrange data in wide and long formats 
[ggplot2](https://ggplot2.tidyverse.org/){target="_blank"}    | to make visualizations with multiple layers
[scales](https://cran.r-project.org/web/packages/scales/scales.pdf){target="_blank"}    | to allow us to look at the colors within the viridis package
[viridis](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html){target="_blank"}    | to make plots with a color palette that is compatible with color blindness
[forcats](https://forcats.tidyverse.org/){target="_blank"}    | to allow for reordering of factors in plots
[syrvr](https://cran.r-project.org/web/packages/srvyr/srvyr.pdf){target="_blank"} | to use survey weights
[cowplot](https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html){target="_blank"} | to allow plots to be combined 


The first time we use a function, we will use the `::` to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.


## **Context**
*** 

According to the cited [Morbidity and Mortality Weekly Report](https://www.cdc.gov/mmwr/volumes/68/wr/mm6806e1.htm?s_cid=mm6806e1_w) this was what was already known about this topic and the implications of this study:

```{r, echo = FALSE, fig.align ="center", out.width = "800 px"}

knitr::include_graphics(here::here("img", "context.png"))

```


Importantly, the vapors used in e-cigarettes contain harmful chemicals:

```{r, echo = FALSE, fig.align ="center"}

include_graphics("https://www.cdc.gov/tobacco/basic_information/e-cigarettes/images/e-cigarette-aerosol-can-contain-harmful-ingredients-desktop-700.jpg")
```

#### [[source](https://www.cdc.gov/tobacco/basic_information/e-cigarettes/images/e-cigarette-aerosol-can-contain-harmful-ingredients-desktop-700.jpg)]{target="_blank"} 

E-cigarette usage has also been associated with [lung injury]((https://www.frontiersin.org/articles/10.3389/fphar.2019.01619/full)){target="_blank"}:

```{r, echo = FALSE, fig.align ="center", out.width = "800 px"}

knitr::include_graphics(here::here("img", "lung.png"))
```

#### [[source](https://www.frontiersin.org/articles/10.3389/fphar.2019.01619/full)]{target="_blank"} 

See [here](https://www.cdc.gov/tobacco/basic_information/e-cigarettes/Quick-Facts-on-the-Risks-of-E-cigarettes-for-Kids-Teens-and-Young-Adults.html){target="_blank"} for additional information about the potential health effects of e-cigarettes in teens and young adults.

## **Limitations**
*** 
There are some important considerations regarding this data analysis to keep in mind: 

1) The data included in the [National Youth Tobacco Survey (NYTS)](https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/index.htm){target="_blank"} does not follow the same individual students over time.  A [longitudinal study](https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/7-longitudinal-studies){target="_blank"} that does follow the same individuals over time collects data called [panel data](https://en.wikipedia.org/wiki/Panel_data). The data in this study is called pooled [cross-sectional data](https://en.wikipedia.org/wiki/Cross-sectional_data), and is obtained from random collection of observations across time.

According to Wikipedia:

> Panel data differs from pooled cross-sectional data across time, because it deals with the observations on the same subjects in different times whereas the latter observes different subjects in different time periods

2) The data include percentages of students reporting use of each particular tobacco product, but the survey questions did not ask the relative amount of use of one product compared to another. For example the survey included questions like:"What flavors of tobacco products have you used in the past 30 days?" but did not ask how often one flavor was used by the same individual over another.

While [gender](https://www.genderspectrum.org/quick-links/understanding-gender/){target="_blank"} and [sex](https://www.who.int/genomics/gender/en/index1.html){target="_blank"} are not actually binary, the data used in this analysis only contain information for groups of individuals who answered the survey questions as male or female. 

## **What are the data?**
*** 
 
The data in this case study comes from the [National Youth Tobacco Survey (NYTS)](https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/index.htm){target="_blank"} which is an annual survey that asks students  in high school and middle school (grades 6-12) about tobacco usage in the United States of America.

The data for this survey is freely available online at this [website](https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/data/index.html){target="_blank"} with data from 1999, 2000, 2002, 2004, 2006, 2009,  and 2011-2019. We will be using data from **2015-2019** due to the fact that these years are the most recent that asked questions regarding e-cigarette usage.

Each year includes documentation, such as a [codebook](https://www.icpsr.umich.edu/icpsrweb/content/shared/ICPSR/faqs/what-is-a-codebook.html) and an excel file containing the data:

```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "data.png"))
```
Therefore, since we are using data from **2015-2019**, the data we are interested in are located in 5 different excel files, one for each year, each with its own [codebook](https://www.icpsr.umich.edu/icpsrweb/content/shared/ICPSR/faqs/what-is-a-codebook.html).

The [codebook](https://www.icpsr.umich.edu/icpsrweb/content/shared/ICPSR/faqs/what-is-a-codebook.html) contains information describing the data within the excel file. 

As you can see the excel file contains very short variable names and numeric values, and it is not clear what they mean without the codebook:

```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "excel.png"))
```

The codebook explains what the variables (the columns) are:
```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "variables.png"))
```

And the codebook explains what the values for each variable are:

```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "qn1.png"))
```

We will explain more later about what the values on the right indicate.

The reason that there are codebooks for each year is because the questions asked each year varied slightly.


The data in this survey are pooled cross-sectional data. In this study design, different subsets of students are surveyed each year and it is not clear which, if any, individuals participate more than once from one year to the next.

## **Data Import**
*** 

### Reading in the excel files
***
Since these excel files are so large (each has roughly 20,000 rows), it takes a bit of time for the data to load. To make the process faster, we previously imported these files, selected only the columns relevant to our questions of interest, and saved these data subsets as comma-separated (.csv) files. 

<details><summary> Click here for details on how the data were originally imported </summary>

First we created a vector of file names of all the different excel files. Using the `here()` function of the `here` package, we looked in all the directories of the project.
The `list.files()` function looked for all files with .xlsx within these sub-directories.
```{r}
excel_files<-list.files(here::here(), recursive = TRUE,
                  pattern = "*.xlsx")
excel_files
```

All the files were read using `read_excel()` of the `readxl` package. Using the `map()` function of the `purrr` package this was done efficiently for all of the excel files in the vector using one command. The `.` is used to indicate that we want to apply the `read_excel()` function to each element of the data that we just piped into the `map()` function.

Here we also used the `%>%` pipe which can be used to pass the input from one function to another for later sequential steps. 

This created a single list of tibbles (one for each file). 
```{r, eval = FALSE}
tbl_files <- excel_files %>% 
       map(~readxl::read_excel(.))
```

The elements of this list are in the same order as the elements of the `excel_files` vector, so we can extract the data for a particular file (year) by selecting a certain element of the list. However, it is safer to be able to select the data from this list for a specific year using a name based on the original vector of file names. We extracted a name from each Excel file name using the `str_extract()` function of the `stringr` package. Here we are keeping occurrences of the character string "nyts201" followed by a "5","6","7","8", or "9".
```{r}
tbl_names <- excel_files %>%
  str_extract("nyts[2][0][1][5-9]")

tbl_names
```

These names became the names of the tibbles in the list of tibbles.
```{r, eval = FALSE}
names(tbl_files) <- tbl_names
```


Specific columns were selected using the `select()` function of `dplyr` from each of the tibbles using the variable name, as identified in the codebook as being of interest for our analysis.
In some cases functions like `starts_with()` of the `dplyr` package were used to select several variables at once. Most of the survey questions about tobacco use start with an `"E"` or a `"C"` according to the codebooks. 

```{r, eval = FALSE}

tbl_files[["nyts2015"]] <- tbl_files[["nyts2015"]] %>%
    dplyr::select(psu, #Primary Sampling Unit
                  finwgt,#Analysis Weight
                  stratum,#Sampling stratum
                  Qn1, #Age
                  Qn2, #Sex
                  Qn3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  )


tbl_files[["nyts2016"]] <- tbl_files[["nyts2016"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  ) 

tbl_files[["nyts2017"]] <- tbl_files[["nyts2017"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  )

tbl_files[["nyts2018"]] <- tbl_files[["nyts2018"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q50A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q50B, #Clove or spice
                  Q50C, #Fruit
                  Q50D, #Chocolate
                  Q50E, #Alcoholic Drink
                  Q50F, #Candy/Desserts/Other Sweets
                  Q50G, #Some Other Flavor Not Listed Here
                  Q50H #I Did Not Use Flavored Tobacco Products In the Past
                  )

tbl_files[["nyts2019"]] <- tbl_files[["nyts2019"]] %>%
    dplyr::select(psu,
                  finwgt,
                  stratum,
                  Q1, #Age
                  Q2, #Sex
                  Q3, #Grade
                  starts_with("E",
                              ignore.case = FALSE),
                  starts_with("C",
                              ignore.case = FALSE),
                  Q40, #Brand, e-cigarettes
                  Q62A, #Menthol # What flavors of tobacco products have you used in the past 30 days? (Select one or more)
                  Q62B, #Clove or spice
                  Q62C, #Fruit 
                  Q62D, #Chocolate
                  Q62E, #Alcoholic Drink
                  Q62F, #Candy/Desserts/Other Sweets
                  Q62G, #Some Other Flavor Not Listed Here 
                  )

```

A directory called `data_reduced` was created for the new .csv files using the base `dir.create()` function. 
A csv file was created for each of the tibbles in the list using the `write_csv()` function of the `readr` package.
This was done all at once using the base `mapply()` function.

```{r, eval = FALSE}
# 
dir.create("docs/data_reduced")

mapply(write_csv, tbl_files, path=here(paste0("docs/data_reduced/",
                                  names(tbl_files),
                                   '.csv'))
     )

#trying to use map...
tbl_files %>%
map(~write_csv(. , path=paste0("docs/data_reduced2/nyts", pull(year)[[1]], ".csv")))


    tbl_files %>%
map(~write_csv(. , path=paste0("docs/data_reduced2/nyts", names(.), ".csv")))

   
```

</details>



Now we will show how to read in the data from the five csv files that were created from the five different Excel files.

### Reading in the CSV files
***

```{r, echo=FALSE, include=FALSE}
start_time <- Sys.time()

end_time <- Sys.time()

test_time <- end_time - start_time

time_message <- paste("Duration of data import:",
      round(as.numeric(test_time) ,3),
      units(test_time)
      )
```


```{r}
nyts_data <- list.files(here::here(),recursive = TRUE,
                   pattern = "*.csv")%>%
  map(~read_csv(.))


nyts_data_names <- list.files(recursive = TRUE,
                         pattern = "*.csv")%>%
  str_extract("nyts[2][0][1][5-9]")

names(nyts_data) <- nyts_data_names
```


## **Data Exploration and Wrangling**
*** 

Our goal in this section is to adjust or "wrangle" the data from each year into a common format so that we can combine the data sets across years for our analysis, and so that we have values in our variables that are correct and easy to interpret. We will need to understand what is the same and what is different across the data from different years, rename and recode the variables (e.g., by replacing the numbers 1 and 2 with the values "Male" and "Female" for the `Sex` variable), and combine the data. We will walk through these steps below. 

First, let's take a look at our data. We can get a good sense of it using the `glimpse()` function of the `dplyr` package.

#### {.scrollable }

```{r}
dplyr::glimpse(nyts_data[["nyts2015"]])
```
####

### Updating the set of variables and their names
*** 

The easiest way of making it so that the data from the different years can be combined is by making sure the different data sets all contain the same variables that share the same names. In addition, giving the columns informative names will help make our code more readable. Currently, it isn't very clear what most of the variables indicate since the variable names are uninformative on their own, without the code book.

We want to rename variables like `Qn1` to something more meaningful like `Age`.

To do this we will use the `rename()` function of the `dplyr` package. The new name is always listed first before the `=`. This function will replace the old variable names with the new ones, i.e., after running the code below, there will no longer be a `Qn1` variable in the data set, but there will be an `Age` variable instead. We will start working with the 2015 data, and then move on to the other years down below.

```{r}

nyts_data[["nyts2015"]] <- nyts_data[["nyts2015"]] %>%
    dplyr::rename(Age=Qn1,
           Sex=Qn2,
           Grade=Qn3)
```


We also need to add new variables about usage of brands and specific flavors of e-cigarettes, because although later years have questions about this, this year unfortunately only has broad questions about flavors. Eventually we want to put the data from each year together, so we need the same variables for each year. Thus we need to add variables for brand and flavor to the 2015 data set that just contain missing values (`NA`).

To create these new variables, we will use the `mutate` function of the `dplyr` package. The `mutate` function can also be used to change the existing variables and create new variables from the old ones.

```{r}
nyts_data[["nyts2015"]] <- nyts_data[["nyts2015"]] %>%
  dplyr::mutate(
           brand_ecig=NA,
           menthol=NA,
           clove_spice=NA,
           fruit=NA,
           chocolate=NA,
           alcoholic_drink=NA,
           candy_dessert_sweets=NA,
           other=NA,
           no_use=NA) 
```


Now we can see how the data has changed:

#### {.scrollable }
```{r}
glimpse(nyts_data[["nyts2015"]])
```
####


The data for 2016-2018 have many common attributes, so we will want to write code that can be applied to all three data sets. To do this, we will use a `function` in R, which is basically a piece of code that can be applied to similar but different objects in R (e.g., the data tibbles from each of these three years). For more information on functions, see for example [here](https://r4ds.had.co.nz/functions.html){target="_blank"}.

These next 3 years have the same structure for many of the questions we are interested in. For example, they all have flavor questions, but not a brand question. Moreover, their variable names are consistent across the years; for each year, we want to replace the vague question name `Q50A` with the value `menthol` in all three data sets, and the same is true for the other flavor variables.  For each of these years, we also want to create a variable `brand_ecig` that just contains `NA` values.  

Since we want to perform the same modifications on the data from all three years, rather than repeating the same somewhat messy piece of code three times, we can do this more efficiently if we create a function to do all of these steps at once. Then we can use the `map_at()` function of the `purrr` package to apply the function we created (for renaming variables etc.) to the data from 2016-2018. By using `vars()` inside of the `map_at()` function we can specify what tibbles within our `nyts_data` list we want to include or exclude.

```{r}

Update_survey <- function(x) { x %>%
      rename(Age=Q1,
           Sex=Q2,
           Grade=Q3,
           menthol=Q50A,
           clove_spice=Q50B,
           fruit=Q50C,
           chocolate=Q50D,
           alcoholic_drink=Q50E,
           candy_dessert_sweets=Q50F,
           other=Q50G,
           no_use=Q50H) %>%
    mutate(brand_ecig=NA)
}

#few options to apply to the data:
#nyts_data <-nyts_data %>% map_at(vars(nyts2016, nyts2017, nyts2018), Update_survey)
#nyts_data <-nyts_data %>% map_at(c("nyts2016", "nyts2017", "nyts2018"), Update_survey)
nyts_data <-nyts_data %>% map_at(vars(-nyts2015, -nyts2019), Update_survey)
```

The final year, 2019, has a slightly different data structure compared to these earlier data sets. For example, it actually has a `brand_ecig` variable already, but does not contain a `no_use` variable. So we can't use the same function for this data set, but have to write an individual code chunk.

```{r}
nyts_data[["nyts2019"]] <- nyts_data[["nyts2019"]] %>%
    rename(brand_ecig=Q40,
           Age=Q1,
           Sex=Q2,
           Grade=Q3,
           menthol=Q62A,
           clove_spice=Q62B,
           fruit=Q62C,
           chocolate=Q62D,
           alcoholic_drink=Q62E,
           candy_dessert_sweets=Q62F,
           other=Q62G) %>%
    mutate(no_use="missing")
```


Now let's take a look at the variable names for each of the years using the `map` function from `purrr`.

#### {.scrollable }

```{r}
map(nyts_data, names)
```
####

It's looking better! There are still some differences in the set of variables in the different years, but things are much closer.

### Updating Values

Now that we have made some progress on the selection and names of the variables themselves, we will work on the values contained in the different variables.

We can start with updating the values for `Age` and `Grade`, so that they are more understandable. 

Recall from the codebook for this year's data set that `Age` isn't listed in the way one might expect, i.e., it is not just a number of years, but a numerically valued categorical variable.

```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "qn1.png"))
```

The same is true for `Grade`:

```{r, echo = FALSE, fig.align ="center", out.width = "600 px"}

knitr::include_graphics(here::here("img", "grade.png"))
```

**This is why it is so important to always check the codebook!!**

We also  want to replace the value of `19` for `Age` to be `">18"` and the value of `13` for `Grade` to be replaced with `"Ungraded/Other"` Also according to the codebooks, numeric values of `1` indicate a survey answer of `FALSE`, while a value of `2` indicates `TRUE`. `Sex` needs to be recoded, but it is a bit unclear if it is encoded slightly differently across years.


Let's create a function to make all these updates except for `Sex`. We will use the `mutate` function again, as well as `mutate_all` and `recode` to replace specific values of certain variables.

```{r}
Update_values <- function(x) { x %>%
      mutate(Age=as.numeric(Age)+8,
             Grade=as.numeric(Grade)+5)%>%
      mutate(Age=as.factor(Age),
         Grade=as.factor(Grade),
         ) %>%
  mutate_all(~ replace(., . %in% c("*", "**"), NA))%>%
  mutate(Age=recode(Age,
                    `19` = ">18",
                    ),
         Grade=recode(Grade,
                      `13` = "Ungraded/Other")) %>%
  mutate_at(vars(starts_with("E", ignore.case = FALSE),
                   starts_with("C", ignore.case = FALSE)),
              list(~recode(.,
                           `1` = TRUE,
                           `2` = FALSE,
                           .default = NA,
                           .missing = NA)))
}

nyts_data <-nyts_data %>% map(.,Update_values)

```


Now let's update the `Sex` encoding:

It's a bit difficult to tell if the encoding was the same across years, so let's check using the `count()` function of the `dplyr` package.

According to the codebook, we should have:  
1) 8,958 males in 2015 
2) 10,438 males in 2016 
3) 8,881 males in 2017  
4) 10,069 males in 2018  
5) 9,803 males in 2019  

```{r}
count_sex <-function(x){x %>%count(Sex)}
nyts_data %>% map(., count_sex)
```


Thus, it looks like males were encoded as `1` for each year.

1) 8,958 males in 2015 where 1 = male  
2) 10,438 males in 2016 where 1 = male
3) 8,881 males in 2017 where 1 = male  
4) 10,069 males in 2018 where 1 = male
5) 9,803 males in 2019 where 1 = male


```{r}

  Update_sex <- function(x){x %>%
      mutate(Sex = as.factor(Sex)) %>%
         mutate(Sex = recode(Sex,
                      `1`= "male",
                      `2` = "female"))
  }

nyts_data <-nyts_data %>% map(., Update_sex)
count_sex <-function(x){x %>%count(Sex)}
nyts_data %>% map(., count_sex)

```

Looks good!



The years (2016-2019) that have flavors also need the flavor data to be logical (meaning TRUE or FALSE):

```{r}
Update_flavors <- function(x){x %>%
   mutate_at(vars(menthol:no_use),
              list(~recode(.,
                           `1` = TRUE,
                       .default = FALSE,
                      .missing = FALSE))) }

nyts_data  <- nyts_data  %>% map_at(vars(-nyts2015), Update_flavors)

```


Now there are just a few changes needed that are specific to 2019:

```{r}
nyts_data[["nyts2019"]] <- nyts_data[["nyts2019"]] %>%
  mutate_all(~ replace(., . %in% c(".N",".S",".Z"), NA)) %>%
  mutate(psu=as.character(psu)) %>%
  mutate(brand_ecig = recode(brand_ecig,
                                             `1` = "Other", #levels 1,8 combined to `Other` 
                                             `2` = "Blu",
                                             `3` = "JUUL",
                                             `4` = "Logic",
                                             `5` = "MarkTen",
                                             `6` = "NJOY",
                                             `7` = "Vuse",
                                             `8` = "Other"))

```

Great! Now we have all the same variables and our values don't need to be handled any differently for any of the years. Thus we can combine the data across years.

```{r}
nyts_data <- nyts_data %>%
  map_df(bind_rows, .id = "year")

glimpse(nyts_data)
```

We will want to do some of our analysis split by year, so we would like to be sure we have one variable that has the correct value for year. It looks like we just need to remove `"nyts"` from the year variable that we created from the names of the tibbles in our list and we should be all set.

```{r}
nyts_data <- nyts_data %>%
  mutate(year=as.numeric(str_remove(year,"nyts")))

```

Here is our clean and wrangled data:

#### .{scrollable}
```{r}
glimpse(nyts_data)

```

Note that there are several variables where there are similar names, but with a `C` compared to an `E` in the variable name. Those starting with `C` are related to questions about current usage (last 30 days), while those with an `E` are related to usage across the subjects whole life ("ever" usage).

####

<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">

Reminder: Current users are a subset of ever users. 

</div>



### Variable Table
<details><summary> Click here to see a table about the variables </summary>

value 1 = yes, value 2 = no

Variable   | Details                                                                        
---------- |-------------
**year**  | the year that the survey results from a particular student were acquired  
**psu** | the primary sampling unit for the survey weighting  
**finwgt** | the final analysis weight for the survey weighting  
**stratum** | the stratum used for variance estimation for the survey weighting  
**Age** | the age of the student when they took the survey  
**Sex** | the sex of the student when they took the survey  
**Grade** | the grade of the the student when the took the survey  
**ECIGT** | student reported having ever tried cigarette smoking, even one or two puffs  
**ECIGAR** | student reported having ever tried cigar, cigarillo, or little cigar smoking, even one or two puffs  
**ESLT** | student reported having ever tried chewing tobacco, snuff, or dip  
**EELCIGT** | student reported having ever tried e-cigarettes  
**EROLLCIGTS** | student reported having ever tried roll-your-own cigarettes  
**EFLAVCIGTS** |  (2015 only) based on answer to "Which of the following tobacco products that you used in the past 30 days were flavored?"  
**EBIDIS** | student reported having ever tried bidis (small brown cigarettes wrapped in a leaf)  
**EFLAVCIGAR** | student reported having ever tried a flavored cigar (2015-2016)
**EHOOKAH** | student reported having ever smoked tobacco from a hookah or a waterpipe  
**EPIPE** | student reported having ever smoked tobacco from a pipe (not hookah)  
**ESNUS** | student reported having ever used snus, such as Camel or Malboro Snus  
**EDISSOLV** | student reported having ever tried dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips  
**CCIGT** | student reported they smoked cigarettes on >= 1 of the past 30 days  
**CCIGAR** | student reported they smoked cigars on >= 1 of the past 30 days  
**CSLT** | student reported they used chewing tobacco, snuff, or dip on >= 1 of the past 30 days  
**CELCIGT** | student reported they used electronic cigarettes or e-cigarettes one or more days in the past 30
**CROLLCIGTS** | student reported they smoked roll-your-own cigarettes during the past 30 days  
**CFLAVCIGTS**| (2015 only) based on answer to "Which of the following tobacco products that you used in the past 30 days were flavored?" 
**CBIDIS** | student reported they smoked bidis during the past 30 days  
**CHOOKAH** | student reported they smoked tobacco in a hookah on >= 1 of the past 30 days  
**CPIPE** | student reported they smoked tobacco in a pipe (not hookah) during the past 30 days  
**CSNUS** | student reported they used snus during the past 30 days    
**CDISSOLV** | student reported they used dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips during the past 30 days  
**brand_ecig** | student answer to "During the past 30 days, what brand of e-cigarettes did you usually use?"  
**menthol** | student selected Menthol or mint as the answer to "What flavors of tobacco products have you used in the past 30 days? (select one or more)"  
**clove_spice** | student selected clove or spice as the answer to "What flavors of tobacco products have you used in the past 30 days? (select one or more)"  
**fruit** | student selected fruit as the answer to "What flavors of tobacco products have you used in the past 30 days? (select one or more)"  
**chocolate** | student selected chocolate as the answer to "What flavors of tobacco products have you used in the past 30 days? (select one or more)"  
**alcoholic_drink** | student selected alcoholic drink (such as wine, cognac, margarita, or other cocktails) as the answer to "What flavors of tobacco products have you used in the past 30 days? (choose one or more)"  
**candy_dessert_sweets** |  student selected candy, desserts or other sweets as the answer to "What flavors of tobacco products have you used in the past 30 days? (choose one or more)" 
**other** | student selected some other flavor not listed as the answer to "What flavors of tobacco products have you used in the past 30 days? (choose one or more)" 
**no_use** | student reported that they did not use any flavored tobacco products in the past 30 days or did not answer the question about using flavors
**EHTP** | student reported having ever tried heated (also known as "heat-not-burn") tobacco products  
**CHTP** | student reported they used heated tobacco products during the past 30 days  
</details>

## **Data Visualization**
*** 

Recall that our main questions were:

1) How has tobacco/nicotine and e-cigarette use by American youths changed since 2015?
2) How do vaping rates compare between males and females?
3) What vaping brands and flavors appear to be used the most frequently?  
We will base this on the following survey questions:   
> "During the past 30 days, what brand of e-cigarettes did you usually use?"   
>" What flavors of tobacco products have you used in the past
30 days?" 

4) Have vaping rates possibly influenced tobacco/nicotine use?


We are now going to create visualizations to explore each of these questions.

For many of these questions we will be interested in both **current** and **ever** users:

We define these two groups based on the survey questions:

1) **current** = students who used the product for >=1 day in the past 30 days  
2) **ever** =  students who report having used or tried a product at any point in time

All **current** users are therefore **ever** users but not all **ever** users are **current** users. Thus, **current** users are a subset of **ever** users.

To add these groups to our data we will do a bit more wrangling. Using the `mutate()` function again of the `dplyr` package, we will first create two new variables that are the sum of the values for questions that relate to the use of any tobacco product surveyed about ever (questions that start with the letter "E"), and questions that relate to the current use of any tobacco products surveyed about (questions that start with the letter "C").

Here are some examples for these data entries:  

 - EPIPE: Students who reported they have smoked tobacco from a pipe (not hookah).  
 - CPIPE: Students who reported they smoked tobacco in a pipe (not hookah) during the past 30 days. 
 - EROLLCIGTS: RECODE: Students who reported they have tried smoking roll-your-own cigarettes. 
 - CROLLCIGTS: RECODE: Students who reported smoked roll-your-own cigarettes during the past 30 days. 
 
Based on many questions like this:  
 
 In the past 30 days, which of the following products have you used on at least one day? (Select one or more) 
A. Roll-your-own cigarettes  
B. Pipes filled with tobacco (not hookah or waterpipe)  
C. Snus, such as Camel, Marlboro, or General Snus  
D. Dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks,
or Camel strips  
E. Bidis (small brown cigarettes wrapped in a leaf)  
F. I have not used any of the products listed above in the past 30 days  

 Which of the following tobacco products have you ever tried, even just one time? (Select one or more)  
A. Roll-your-own cigarettes  
B. Pipes filled with tobacco (not hookah or waterpipe)  
C. Snus, such as Camel, Marlboro, or General Snus  
D. Dissolvable tobacco products such as Ariva, Stonewall, Camel orbs, Camel sticks, Marlboro sticks, or Camel strips  
E. Bidis (small brown cigarettes wrapped in a leaf)  
F. I have never tried any of the products listed above   
 
 
We will sum across the variables that relate to ever or current tobacco usage questions to get determine if the student answered yes to any of the ever or current questions. 

We will then use the `case_when()` function of the `dplyr` package to convert the sum values to `TRUE` or `FALSE` based on the threshold of zero. If the sum is greater than zero, then we know the student answered yes to at least one question. 
 

```{r}
nyts_data <-nyts_data %>%
    mutate(tobacco_sum_ever = select(., starts_with("E", ignore.case = FALSE)) %>%
               base::apply(1, sum, na.rm=TRUE),
           tobacco_sum_current = select(., starts_with("C", ignore.case = FALSE)) %>%
               apply(1, sum, na.rm=TRUE)) %>%
    mutate(tobacco_ever = case_when(tobacco_sum_ever > 0 ~ TRUE,
                                    tobacco_sum_ever == 0 ~ FALSE),
           tobacco_current = case_when(tobacco_sum_current > 0 ~ TRUE,
                                    tobacco_sum_current == 0 ~ FALSE)) 

```


#### {.scrollable}
```{r}
glimpse(nyts_data)
```
####

We are also interested in e-cigarette usage compared to other tobacco products, so we will create some variables related to the sum of all e-cigarette usage question variables and the sum of all tobacco usage question variables excluding those that are about e-cigarettes. There is only one variable about e-cigarette usage ever (EELCIGT) and one about current usage (CELCIGT).

```{r}
   nyts_data <-nyts_data %>% mutate(ecig_sum_ever = select(., EELCIGT) %>%
               apply(1, sum, na.rm=TRUE),
           ecig_sum_current = select(., CELCIGT) %>%
               apply(1, sum, na.rm=TRUE),
           non_ecig_sum_ever = select(., starts_with("E", ignore.case = FALSE)) %>%
               select(.,-EELCIGT) %>%
               apply(1, sum, na.rm=TRUE),
           non_ecig_sum_current = select(., starts_with("C", ignore.case = FALSE)) %>%
               select(.,-CELCIGT) %>%
               apply(1, sum, na.rm=TRUE)) %>%
    mutate(ecig_ever = case_when(ecig_sum_ever > 0 ~ TRUE,
                                    ecig_sum_ever == 0 ~ FALSE),
           ecig_current = case_when(ecig_sum_current > 0 ~ TRUE,
                                    ecig_sum_current == 0 ~ FALSE),
           non_ecig_ever = case_when(non_ecig_sum_ever > 0 ~ TRUE,
                                    non_ecig_sum_ever == 0 ~ FALSE),
           non_ecig_current = case_when(non_ecig_sum_current > 0 ~ TRUE,
                                    non_ecig_sum_current == 0 ~ FALSE))
```



#### {.scrollable}
```{r}
glimpse(nyts_data)
```

Finally, we are also interested in students that only use e-cigarettes and those that only other forms of tobacco.
We will again use the case_when function to accomplish creating new variables. 

Here is a toy example of what we will do with the larger dataset:

Recall that current users are a subset of ever users, thus students would typically answer yes to having tried vaping products if they had used them one or more days in the past 30 days.

First we will make a small toy dataset:
```{r}
test <- tibble(ecig_ever = c("TRUE", "TRUE", "TRUE", "TRUE", "FALSE", "FALSE", "TRUE", "FALSE", "FALSE"),
               non_ecig_ever = c("TRUE", "FALSE","FALSE","FALSE", "FALSE", "FALSE", "TRUE", "TRUE", "TRUE"),
               ecig_current = c("TRUE", "FALSE","FALSE", "TRUE", "TRUE", "FALSE", "FALSE", "FALSE", "FALSE"),
               non_ecig_current = c("TRUE", "FALSE","TRUE", "FALSE", "TRUE", "FALSE", "FALSE", "FALSE", "TRUE"))

test
```

Now, let's look at identifying sutents who have only ever tried e-cigarettes, but are not current users, nor have they tried or currently use other tobacco products.

```{r}

test <- test %>% mutate(ecig_only_ever = case_when(ecig_ever == TRUE & 
                                               non_ecig_ever == FALSE & 
                                                ecig_current == FALSE &
                                            non_ecig_current == FALSE ~ TRUE,
              #if any of the last three lines are TRUE than the ecig_only_ever value should be false
                                                    TRUE ~ FALSE))
test
```

We can see from the fourth row, that even though the student reported yes to ever trying e-cigarettes, they also reported yes to currently using e-cigarettes, thus the value for only ever trying e-cigarettes is `FALSE`. Additionally we can see from the seventh row that similarly even though the student reported yest to ever trying e0cigarettes, they also reported yes to ever trying other products, and the value for only ever trying e-cigarettes is `FALSE`. Importantly, we can see from the 6th row, that if all responses are negative than the value is `FALSE`.

Now we will expand this to the other possible categories. Notice since, current users are a subset of ever users, it doesn't matter if a user reports yes to ever trying  e-cigarettes, they can still be a current user.

```{r}
test <-test %>% mutate(ecig_only_ever = case_when(ecig_ever == TRUE & 
                                              non_ecig_ever == FALSE & 
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~ TRUE,
              #if any of the last three lines are TRUE than the ecig_only_ever value should be false
                                                       TRUE ~ FALSE),
                 ecig_only_current = case_when(ecig_current == TRUE &
                                              non_ecig_ever == FALSE &
                                           non_ecig_current == FALSE ~ TRUE,
                                                       TRUE ~ FALSE),
               non_ecig_only_ever = case_when(non_ecig_ever == TRUE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~ TRUE, 
                                                        TRUE ~ FALSE),
         non_ecig_only_current = case_when(non_ecig_current == TRUE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE ~ TRUE,
                                                        TRUE ~ FALSE),
                           no_use = case_when(non_ecig_ever == FALSE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~TRUE,
                                                        TRUE ~ FALSE)) 

test
```

Take a minute to check that the values are what we would expect.

Ok, now we are going to make a `Group` variable based on the new variables we just made. If a student has only ever tried or currently uses e-cigarettes, the value will be `Only e-cigarettes`. The same will be true for other tobacco products. If a student has tried either they will be called `Both`.

```{R}

test <- test %>% 
  mutate(Group = case_when(ecig_only_ever == TRUE |
                        ecig_only_current == TRUE ~ "Only e-cigarettes",
                       non_ecig_only_ever == TRUE |
                    non_ecig_only_current == TRUE ~ "Only other products",
                                   no_use == TRUE ~ "Neither",
                                     TRUE ~ "Both"))

test

```

```{r}
nyts_data %<>%
    mutate(ecig_only_ever = case_when(ecig_ever == TRUE & 
                                              non_ecig_ever == FALSE & 
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~ TRUE,
              #if any of the last three lines are TRUE than the ecig_only_ever value should be false
                                                       TRUE ~ FALSE),
                 ecig_only_current = case_when(ecig_current == TRUE &
                                              non_ecig_ever == FALSE &
                                           non_ecig_current == FALSE ~ TRUE,
                                                       TRUE ~ FALSE),
               non_ecig_only_ever = case_when(non_ecig_ever == TRUE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~ TRUE, 
                                                        TRUE ~ FALSE),
         non_ecig_only_current = case_when(non_ecig_current == TRUE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE ~ TRUE,
                                                        TRUE ~ FALSE),
                           no_use = case_when(non_ecig_ever == FALSE &
                                                  ecig_ever == FALSE &
                                               ecig_current == FALSE &
                                           non_ecig_current == FALSE ~TRUE,
                                                        TRUE ~ FALSE)) %>%
                    mutate(Group = case_when(ecig_only_ever == TRUE |
                                          ecig_only_current == TRUE ~ "Only e-cigarettes",
                                         non_ecig_only_ever == TRUE |
                                      non_ecig_only_current == TRUE ~ "Only other products",
                                                     no_use == TRUE ~ "Neither",
                                                        TRUE ~ "Both"))
```

#### {.scrollable}
```{r}
glimpse(nyts_data)
```

### Question 1 

We are interested in investing how vaping product use has compared with other tobacco use. First we want to get a sense of how tobacco use has changed in general since 2015. 

To create a visualiztion about how tobacco usage has changed over time, we will first convert the usage data to a mean value for each year. To do this we will use the `group_by()` and `summarize` function of the `dplyr` package. This will create new variables which we will name `Ever` and `Current` based on the mean of the percentages of `tobacco_ever` and `tobacco_current` for each year. 

```{r}

nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize(Ever=(mean(tobacco_ever, na.rm = TRUE)*100),
                     Current=(mean(tobacco_current, na.rm = TRUE)*100))

```
We will use the `pivot_longer` function to take all collumns except year (in this case the `Ever` and `Current` columns), to create a column called `User` that will contain the current column name information and a column called `Percentage of students` which will contain the mean percentage values that we just calculated. This converts our data into a format called long format.

```{r}

nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize(Ever=(mean(tobacco_ever, na.rm = TRUE)*100),
                     Current=(mean(tobacco_current, na.rm = TRUE)*100))%>%
   pivot_longer(cols = -year, names_to = "User", values_to = "Percentage of students")

```
You may have noticed that our data is longer than it used to be! Hence the name of the function `pivot_longer()`. Data is often easier to plot when it is in this format.
  
Now we will use this data to create a plot using the `ggplot2` package. 

The first thing we do to create a plot is specify what data we are using for our x axis and y axis with the`aes()` argument of the `ggplot()` function. Then we add layers to our plot that specify what type of plot we would like to create. We can use the `geom_line()` function to create lines for each type of user. We specify that we want to use different line types for each user using `aes()`. We will also add points to our lines using the `geom_point()` function. We can add additional layers to specify colors and details about labels and legends etc.
  
```{r}  
  
plot1 <- nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize(Ever=(mean(tobacco_ever, na.rm = TRUE)*100),
                     Current=(mean(tobacco_current, na.rm = TRUE)*100))%>%
  pivot_longer(cols = -year, names_to = "User", values_to = "Percentage of students")%>%
    ggplot(aes(x=year,y=`Percentage of students`)) +
    geom_line(aes(linetype=User)) + 
  geom_point(show.legend = FALSE, size = 3) +
  # this allows us to choose what type of line we want for each line
  scale_linetype_manual(values = c(2,1)) +
  # this allows us to specify how the y-axis should appear
  # scale_y_continuous(breaks = seq(0,60,by=10),
  #                      labels = seq(0,60,by=10),
  #                      limits = c(0,60)) +
  #this adjusts the background style of the plot
    theme_minimal() +
  # this moves the legend to the bottom of the plot and removes the x axis title
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How has tobacco use varied over the years?",
         y = "% of students")

plot1 
```

Nice! Now we can see how overall tobacco usage has changed since 2017. It appears that usage has increased a bit since 2017, and has surpassed the levels in 2015.

What about e-cigarette use? How has the usage of e-cigarettes changed over time?

```{r}
plot1a <- nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize(Ever=(mean(ecig_ever, na.rm = TRUE)*100),
                     Current=(mean(ecig_current, na.rm = TRUE)*100))%>%
  pivot_longer(cols = -year, names_to = "User", values_to = "Percentage of students")%>%
    ggplot(aes(x=year,y=`Percentage of students`)) +
    geom_line(aes(linetype=User)) + 
  geom_point(show.legend = FALSE, size = 3) +
  # this allows us to choose what type of line we want for each line
  scale_linetype_manual(values = c(2,1)) +
  # this allows us to specify how the y-axis should appear
  # scale_y_continuous(breaks = seq(0,60,by=10),
  #                      labels = seq(0,60,by=10),
  #                      limits = c(0,60)) +
  #this adjusts the background style of the plot
    theme_minimal() +
  # this moves the legend to the bottom of the plot and removes the x axis title
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How has e-cigarette use varied over the years?",
         y = "% of students")

plot1a 



```
It looks like the shape of the plot is very similar to tobacco usage overall. We see a downward trend until 2017 when the rate of both current and ever users increased. Recall that this is in agreement with the articles that we referenced earlier. We can see that the slope looks steeper for e-cigarette usage as compared to all tobacco products (including e-cigarettes).

Now let's plot e-cigarette usage and tobacco products usage (besides e-cigarettes) together on the same plot. In this case we will look at students who have only smoked either of e-cigarettes or only other tobacco products besides e-cigarettes.

In this case we will have four groups (only e-cigarette ever users, only e-cigarette current users, only non-e-cigarette ever tobbaco users, and only non-e-cigarette current tobacco users) to plot, therefore, it would be useful to add color to our plot.

One important thing to keep in mind when creating plots, is that individuals with color blindness may have a difficult time distinguishing groups when certian color chocies are used. 

One great option is to use the `viridis` package, which offers color palattes with colors that are still distinguishable  by individuals with most forms of color blindness. 

 We can choose which colors we want to use by using the `show_col()` function of the `scales` package.

Here are some color options:
```{r}

scales:: show_col(viridis_pal()(6))
v_colors =  viridis(6)[c(1,4)]
```

We will select the first and fourth colors for our plot. To add these specific colors we will use the `scale_color_manual()` function of the `ggplot2` package.

We will calculate the mean ever and current usage percentages for students who had only used e-cigarette or other tobacco products (besides e-cigarettes) for each year again using the `group_by()` function and the `summarize()` function. We will again use the `pivot_longer` function to convert our data to long format. We will also use the `separate()` function of the `tidyr` package to create two variables from one of variables. This is done by separating by, in this case, a space. 

```{r}

nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize("Ever e-cig"=(mean(ecig_only_ever, na.rm = TRUE)*100),
                     "Current e-cig"=(mean(ecig_only_current, na.rm = TRUE)*100),
                     "Ever non-e-cig" =(mean(non_ecig_only_ever, na.rm = TRUE)*100),
                     "Current non-e-cig"=(mean(non_ecig_only_current, na.rm = TRUE)*100)) %>%
  pivot_longer(cols = -year, names_to = "User", values_to = "Percentage of students") %>%
  tidyr::separate(User, into = c("User", "Product"), sep = " ") %>%
  head()

plot1c <- nyts_data %>%
    dplyr::group_by(year) %>%
    dplyr::summarize("Ever e-cig"=(mean(ecig_only_ever, na.rm = TRUE)*100),
                     "Current e-cig"=(mean(ecig_only_current, na.rm = TRUE)*100),
                     "Ever non-e-cig" =(mean(non_ecig_only_ever, na.rm = TRUE)*100),
                     "Current non-e-cig"=(mean(non_ecig_only_current, na.rm = TRUE)*100))%>%
  pivot_longer(cols = -year, names_to = "User", values_to = "Percentage of students")%>%
  separate(User, into = c("User", "Product"), sep = " ") %>%
    ggplot(aes(x=year,y=`Percentage of students`, color = Product)) +
    geom_line(aes(linetype=User)) + 
  geom_point(show.legend = FALSE, size = 3) +
  # this allows us to choose what type of line we want for each line
  scale_linetype_manual(values = c(2,1)) +
  # this allows us to specify how the y-axis should appear
  # scale_y_continuous(breaks = seq(0,30,by=10),
  #                      labels = seq(0,30,by=10),
  #                      limits = c(0,30)) +
  scale_color_manual(values = v_colors)+
  #this adjusts the background style of the plot
    theme_minimal() +
  # this moves the legend to the bottom of the plot and removes the x axis title
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How has e-cigarette use and tobacco use (besides e-cigarettes) varied over time?",
         y = "% of students")

plot1c

```

We can see from this plot that the percentage of students who had only ever tried (or currently used) e-cigarettes was greatly increasing since 2017, while in contrast the percentage of students who had only ever tried non-e-cigarette tobacco products was  diminishing over time. In fact, we can see that in 2019 the percentage of students who were current e-cigarette users surpassed the percentage that had tried a non-e-cigarette product even just once. 

We will explore the relaitonship between e-cigarette usage and other tobacco products a bit later in the case study.


Recall that we made a variable called `Group` that identified students who used either just e-cigarette products, just other tobacco products (besides e-cigarettes), or students who used both e-cigarettes and some other type of tobacco product.

```{r}
nyts_data %>%
  count(Group)
```

We will now make a plot over time of each of these groups.

```{r}
nyts_data %>%
    count(Group, year) %>%
    head()


nyts_data %>%
    count(Group, year) %>%
  ggplot(aes(x = year, y = n, fill = Group)) +
  geom_bar(stat = "identity")




```

### Question 2

Now we want to look how e-cigarette smoking rates compare between males and females across time. 


We will calculate the mean e-cigarette ever and current percentages for each year and sex category again using the `group_by()` function and the `summarize()` function. Notice, that in this case we are grouping by two variables by simply using `group_by(year, Sex)`. We will again use the `pivot_longer` function to convert our data to long format.

```{r}

nyts_data %>%
     group_by(year, Sex) %>%
     summarize(Ever=(mean(EELCIGT, na.rm = TRUE)*100),
               Current=(mean(CELCIGT, na.rm = TRUE)*100))%>%
     #filter(!is.na(Sex)) %>%
     pivot_longer(cols = Ever:Current, 
               names_to = "User", 
               values_to = "Percentage of students") %>%
     head()

plot2 <- nyts_data %>%
     group_by(year, Sex) %>%
     summarize(Ever=(mean(EELCIGT, na.rm = TRUE)*100),
               Current=(mean(CELCIGT, na.rm = TRUE)*100))%>%
    # filter(!is.na(Sex)) %>%
     pivot_longer(cols = Ever:Current, 
               names_to = "User", 
               values_to = "Percentage of students") %>%
    ggplot(aes(x = year,y =`Percentage of students`, color = Sex)) +
    geom_line(aes(linetype = User)) + 
    geom_point(show.legend = FALSE, size = 3) +
    scale_linetype_manual(values = c(2,1)) +
    scale_color_manual(values =v_colors)+
    theme_minimal() +
    theme(legend.position = "bottom",
          axis.text.x = element_text(angle = 90),
          axis.title.x = element_blank()) +
    labs(title = "How do vaping rates compare between males and females?",
         subtitle = "Current and ever users by sex",
         y = "% of students")

plot2
```

It looks like the rates are fairly similar between the sexes, however the rate for males appears to be consistently higher across time.


### Question 3

We are also interested in what vaping brands and flavors appear to be used the most frequently. Only the 2019 data has this information. Therefore, we will filter for just this year using the `filter()` function of  the `dplyr` package. We will use the `summarize()` function slightly differently this time, to calculate the total number of students using each brand `n` using the `n()` function and the `sum()` function to calculate the percent for each brand based on the counts. 


```{r}

 nyts_data %>%
    filter(year==2019) %>%
    group_by(brand_ecig) %>%
    filter(!is.na(brand_ecig)) %>%
  summarize(n = n()) %>%
    mutate(total = sum(n),
           Percent = n*100/total) %>%
    mutate(brand_ecig = fct_reorder(brand_ecig, desc(Percent)))


plot3 <- nyts_data %>%
    filter(year==2019) %>%
    group_by(brand_ecig) %>%
    filter(!is.na(brand_ecig)) %>%
  summarize(n = n()) %>%
    mutate(total = sum(n),
           Percent = n*100/total) %>%
    mutate(brand_ecig = fct_reorder(brand_ecig, desc(Percent))) %>%
    ggplot(aes(x=brand_ecig,y=Percent, fill=brand_ecig)) +
    geom_bar(stat="identity", color = "black") +
    theme_minimal() +
    theme(legend.position = "none",
          axis.text.x = element_text(size=10),
          axis.title.x = element_blank()) +
    labs(title = "What vaping brands appear to be used the most frequently?",
         subtitle = "Brand of e-cigarette most frequently used in the last 30 days (2019)",
         y = "% of e-cigarette users responding")

plot3
```

Juul appears to be the most widely used brand. This is in agreement with a recent [article](https://tobaccocontrol.bmj.com/content/tobaccocontrol/28/2/146.full.pdf):

```{r, echo=FALSE, fig.cap="Huang J, Duan Z, Kwok J, et al. Tob Control 2019;28:146–151.", out.width = '100%'}
knitr::include_graphics(here::here("img", "HuangJ_DuanZ_KwokJ_et_al_TobaccoControl_Figure1.png"))
```

We are also interested in how the usage of different flavors has changed over the years. 
To evaluate this we will calculate the precentage of students using each flavor each year - this includes usage of any type of flavored tobacco product.

```{r}
plot4 <- nyts_data %>%
  filter(year!=2015) %>%
  group_by(year) %>%
summarize(Menthol=(mean(menthol, na.rm = TRUE)*100),
         `Clove or Spice`=(mean(clove_spice, na.rm = TRUE)*100),
              `Fruit`=(mean(fruit, na.rm = TRUE)*100),
              `Chocolate`=(mean(chocolate, na.rm = TRUE)*100),
              `Alcoholic Drink`=(mean(alcoholic_drink, na.rm = TRUE)*100),
              `Candy/Desserts/Sweets`=(mean(candy_dessert_sweets, na.rm = TRUE)*100)) %>%
  pivot_longer(cols = -year, names_to = "Flavor", values_to = "Percentage of students") %>%

 ggplot(aes(y = `Percentage of students`, 
            x = year, 
            fill = reorder(Flavor, `Percentage of students`)))+
 geom_bar(stat = "identity", position = "dodge", color = "black")+
  scale_fill_viridis(discrete=TRUE)+
  theme_minimal() +
  guides(fill=guide_legend("Flavor"))+
  labs(title = "What flavors appear to be used the most frequently?",
       subtitle = "Flavors of tobacco products used in the past 30 days")

plot4 
```

From this plot, we can see that fruit flavors are the most widely used products, followed by menthol or mint flavored products. 

We will now look specificially at the usage of flavored e-cigarette products vs other flavored tobacco products. 

Recall that we made a variable called `Group` that identified students who used either just e-cigarette products, just other tobacco products (besides e-cigarettes), or students who used both e-cigarettes and some other type of tobacco product.

```{r}
count(nyts_data, Group)
```

The majority of the students that use tobacco use both e-cigarettes and other products.

```{r}
plot5 <- nyts_data %>%
  filter(year != 2015) %>%
  # filter(menthol == TRUE|
  #          clove_spice == TRUE|
  #          fruit == TRUE|
  #          chocolate == TRUE|
  #          alcoholic_drink == TRUE|
  #          candy_dessert_sweets == TRUE|
  #          other == TRUE) %>%
# we want to remove students that used both e-cigarette and other tobacco products
  filter(Group != "Both") %>%
  group_by(year, Group) %>%
summarize(Menthol=(mean(menthol, na.rm = TRUE)*100),
         `Clove or Spice`=(mean(clove_spice, na.rm = TRUE)*100),
          Fruit=(mean(fruit, na.rm = TRUE)*100),
          Chocolate=(mean(chocolate, na.rm = TRUE)*100),
          `Alcoholic Drink`=(mean(alcoholic_drink, na.rm = TRUE)*100),
          `Candy/Desserts/Sweets`=(mean(candy_dessert_sweets, na.rm = TRUE)*100),
          Other = (mean(other, na.rm = TRUE)*100),
          Respondents=n()) %>%
  #converting all columns between and including Menthol and Other to one column called Flavor
  pivot_longer(cols = Menthol:Other, names_to = "Flavor", values_to = "Percentage of students") %>%
  filter(!is.na(`Percentage of students`),
         Flavor!="Other") %>%
  group_by(Flavor) %>%
  mutate(affirmative=(Respondents * `Percentage of students`)/100) %>%
  mutate(flavor_mean = sum(affirmative)/sum(Respondents)) %>%
  ungroup() %>%
  mutate(flavor_mean_rank = dense_rank(flavor_mean),
         Flavor = fct_reorder(Flavor, flavor_mean_rank)) %>%
  ggplot(aes(x=year, y=`Percentage of students`, color=Group)) +
  facet_wrap(.~Flavor,ncol=3) +
  geom_line() + 
  geom_point(show.legend = FALSE) + 
  theme_minimal() +
  theme(legend.position = "bottom",
          axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 90)) + 
  labs(title = "Among users of only one type of product, what vaping flavors appear to be used the most frequently?",
       subtitle = "Percent reporting only e-cigarette use vs only other nicotine product use")

plot5
```

### Question 4

Have vaping rates possibly influenced tobacco/nicotine use?

```{r}
plot6 <- nyts_data %>%
    group_by(year) %>%
  summarize(ECIGT_year=(sum(ECIGT, na.rm = TRUE)*100)/
                sum(!is.na(ECIGT)),
              EELCIGT_year=(sum(EELCIGT, na.rm = TRUE)*100)/
                sum(!is.na(EELCIGT)),
              CCIGT_year=(sum(CCIGT, na.rm = TRUE)*100)/
                sum(!is.na(CCIGT)),
              CELCIGT_year=(sum(CELCIGT, na.rm = TRUE)*100)/
                sum(!is.na(CELCIGT))) %>%
    rename("Cigarettes, Ever"=ECIGT_year,
           "E-cigarettes, Ever"=EELCIGT_year,
           "Cigarettes, Current"=CCIGT_year,
           "E-cigarettes, Current"=CELCIGT_year) %>%
  pivot_longer(cols= -year, names_to = "Category", values_to = "Percentage of students")%>%
    mutate(User = case_when(Category == "Cigarettes, Ever" ~ "Ever",
                               Category == "E-cigarettes, Ever" ~ "Ever",
                               Category == "Cigarettes, Current" ~ "Current",
                               Category == "E-cigarettes, Current" ~ "Current")) %>%
    mutate(Product = case_when(Category == "Cigarettes, Ever" ~ "Cigarettes",
                               Category == "E-cigarettes, Ever" ~ "E-cigarettes",
                               Category == "Cigarettes, Current" ~ "Cigarettes",
                               Category == "E-cigarettes, Current" ~ "E-cigarettes")) %>%
    ggplot(aes(x=year,y=`Percentage of students`, color=Product, linetype=User)) +
    geom_line() + 
  geom_point(show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
    theme_minimal() +
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How does e-cigarette use compare to cigarette use?",
         subtitle = "Current and ever users of e-cigarettes and cigarettes",
         y = "% of students")

plot6
```

```{r}
plot7 <- nyts_data %>%
    group_by(year) %>%
  summarize(ecig_ever_year=(sum(ecig_ever, na.rm = TRUE)*100)/
                sum(!is.na(ecig_ever)),
              ecig_current_year=(sum(ecig_current, na.rm = TRUE)*100)/
                sum(!is.na(ecig_current)),
              non_ecig_ever_year=(sum(non_ecig_ever, na.rm = TRUE)*100)/
                sum(!is.na(non_ecig_ever)),
              non_ecig_current_year=(sum(non_ecig_current, na.rm = TRUE)*100)/
                sum(!is.na(non_ecig_current))) %>%
    pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
    mutate(User = case_when(Category =="ecig_ever_year" ~ "Ever",
                           Category =="non_ecig_ever_year" ~ "Ever",
                           Category =="ecig_current_year" ~ "Current",
                           Category =="non_ecig_current_year" ~ "Current")) %>%
    mutate(Product = case_when(Category =="ecig_ever_year" ~ "E-cigarettes",
                           Category =="non_ecig_ever_year" ~ "Other products",
                           Category =="ecig_current_year" ~ "E-cigarettes",
                           Category =="non_ecig_current_year" ~ "Other products")) %>%
    filter(User=="Ever") %>%
    ggplot(aes(x=year,y=`Percentage of students`, color=Product)) +
    geom_line(linetype=1) + # geom_bar(stat="identity", position = "dodge", color="black") +
  geom_point(show.legend = FALSE) +
  scale_y_continuous(breaks = seq(10, 60, by = 10), limits = c(10,60)) +
    theme_minimal() +
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How does e-cigarette ever use compare to ever use of other products over the years?",
         y = "% of students")

plot7
```

```{r}
plot8 <- nyts_data %>%
    group_by(year) %>%
  summarize(ecig_ever_year=(sum(ecig_ever, na.rm = TRUE)*100)/
                sum(!is.na(ecig_ever)),
              ecig_current_year=(sum(ecig_current, na.rm = TRUE)*100)/
                sum(!is.na(ecig_current)),
              non_ecig_ever_year=(sum(non_ecig_ever, na.rm = TRUE)*100)/
                sum(!is.na(non_ecig_ever)),
              non_ecig_current_year=(sum(non_ecig_current, na.rm = TRUE)*100)/
                sum(!is.na(non_ecig_current))) %>%
    pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
    mutate(User = case_when(Category =="ecig_ever_year" ~ "Ever",
                           Category =="non_ecig_ever_year" ~ "Ever",
                           Category =="ecig_current_year" ~ "Current",
                           Category =="non_ecig_current_year" ~ "Current")) %>%
    mutate(Product = case_when(Category =="ecig_ever_year" ~ "E-cigarettes",
                           Category =="non_ecig_ever_year" ~ "Other products",
                           Category =="ecig_current_year" ~ "E-cigarettes",
                           Category =="non_ecig_current_year" ~ "Other products")) %>%
    ggplot(aes(x=year,y=`Percentage of students`, color=Product, linetype=User)) +
    geom_line() +
  geom_point(show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
  scale_y_continuous(breaks = seq(0, 60, by = 10), limits = c(0,60)) +
    theme_minimal() +
    theme(legend.position = "bottom",
          axis.title.x = element_blank()) +
    labs(title = "How has e-cigarette use compared to the use of other tobacco products?",
         y = "% of students")

plot8
```

### Unweighted Sample

```{r, fig.height=10}
plotA_uw <- plot1 +
  theme(axis.title.x = element_blank(),
        legend.position = "none") +
    labs(title = "Nicotine product users more prevalent after 2017",
         subtitle = NULL,
         y = "% of students")

plotB_uw <- plot7 + 
  theme(axis.title.x = element_blank(),
        legend.position = "none") +
    labs(title = "% Ever trying e-cigarettes increases &\n% ever trying other products decreases",
         subtitle = NULL,
         y = "% of students")

plotC_uw <- plot8 + 
  theme(axis.title.x = element_blank(),
        legend.position = "none") +
    labs(title = "% Using e-cigarettes increases &\n% using Other products decreases",
         subtitle = NULL,
         y = "% of students")

title_uw <- ggdraw() + 
  draw_label(
    "Have vaping rates possibly influenced tobacco/nicotine use?",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_uw <- plot_grid(plotA_uw,
                     rel_widths = c(1,1))
plotsBC_uw <- plot_grid(plotB_uw,
                        plotC_uw,
                        rel_widths = c(1,1))

legend_uw <- get_legend(plot8 +
                       theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

figure_uw <- plot_grid(title_uw,
                       plotsA_uw,
                       plotsBC_uw,
                       legend_uw,
                       ncol = 1,
                       rel_heights = c(0.1,
                                       1,
                                       1,
                                       0.1),
                       scale = 1.0)

figure_uw
```

### Weighted Sample
```{r}
plotA_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
summarize(tobacco_ever_year = survey_mean(tobacco_ever, vartype = "ci", na.rm=TRUE),
            tobacco_current_year = survey_mean(tobacco_current, vartype = "ci", na.rm=TRUE))  %>%
  mutate_at(vars(-year), "*", 100) %>%
    pivot_longer(cols = -year, names_to = "Type", values_to = "Percentage of students") %>%
  mutate(Estimate = case_when(grepl("_low", Type) ~ "Lower",
                          grepl("_upp", Type) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Type) ~ "Ever",
                          grepl("current", Type) ~ "Current",
                          TRUE ~ "Mean")) %>%
  dplyr::select(-Type) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  ggplot(aes(x=year,y=Mean)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
    scale_y_continuous(breaks = seq(0,70,by=10),
                       labels = seq(0,70,by=10),
                       limits = c(0,70)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "Nicotine product users more prevalent after 2017",
         y = "% of students")

plotB_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
  summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
  mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("current", Category) ~ "Current",
                          TRUE ~ "Ever",),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  filter(User=="Ever") %>%
  dplyr::rename("Lower_temp" = Upper,
                "Upper_temp" = Lower) %>%
  dplyr::rename("Lower"=Lower_temp,
                "Upper"=Upper_temp) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(linetype=1) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_y_continuous(breaks = seq(10, 60, by = 10), limits = c(10,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% ever trying e-cigarettes increases &\n% ever trying other products decreases",
         y = "% of students")

#### the wrangling looks the same as the above plot...
plotC_w <- nyts_data %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt, nest=TRUE) %>%
    group_by(year) %>%
summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students") %>%
    mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Category) ~ "Ever",
                          grepl("current", Category) ~ "Current"),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), show.legend = FALSE) +
  scale_linetype_manual(values = c(2,1)) +
  scale_y_continuous(breaks = seq(0, 60, by = 10), limits = c(0,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% Using e-cigarettes increases &\n% using Other products decreases",
         y = "% of students")

title_w <- ggdraw() + 
  draw_label(
    expression("Have vaping rates possibly influenced tobacco/nicotine use?"),
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_w <- plot_grid(plotA_w,
                     rel_widths = c(1),
                     align = "v",
                     axis = "bt")
plotsBC_w <- plot_grid(plotB_w,
                     plotC_w,
                     rel_widths = c(1,1),
                     align = "v",
                     axis = "bt")

legend_w <- get_legend(plot8 +
                       theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

figure_w <- plot_grid(title_w,
                      plotsA_w,
                      plotsBC_w,
                      legend_w,
                      ncol = 1,
                      rel_heights = c(0.1,
                                      1,
                                      1,
                                      0.1),
                      scale = 1.0)

figure_w
```

### Hypothetical Cohort
```{r}
plotA_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
summarize(tobacco_ever_year = survey_mean(tobacco_ever, vartype = "ci", na.rm=TRUE),
            tobacco_current_year = survey_mean(tobacco_current, vartype = "ci", na.rm=TRUE))  %>%
  mutate_at(vars(-year), "*", 100) %>%
    pivot_longer(cols = -year, names_to = "Type", values_to = "Percentage of students")%>%
  mutate(Estimate = case_when(grepl("_low", Type) ~ "Lower",
                          grepl("_upp", Type) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Type) ~ "Ever",
                          grepl("current", Type) ~ "Current",
                          TRUE ~ "Mean")) %>%
  dplyr::select(-Type) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  #spread(Estimate, `Percentage of students`) %>%
  ggplot(aes(x=year,y=Mean)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_linetype_manual(values = c(2,1)) +
    scale_y_continuous(breaks = seq(0,70,by=10),
                       labels = seq(0,70,by=10),
                       limits = c(0,70)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "Nicotine product users becoming increasingly prevalent",
         y = "% of students")

plotB_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
  summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
   pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students")%>%
  mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("current", Category) ~ "Current",
                          TRUE ~ "Ever",),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
  filter(User=="Ever") %>%
  dplyr::rename("Lower_temp" = Upper,
                "Upper_temp" = Lower) %>%
  dplyr::rename("Lower"=Lower_temp,
                "Upper"=Upper_temp) %>%
  ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(linetype=1) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_y_continuous(breaks = seq(10, 60, by = 10), limits = c(10,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "% ever trying nicotine products increases",
         y = "% of students")

plotC_w_8 <- nyts_data %>%
  filter((Grade == "8" & year == 2015) |
         (Grade == "9" & year == 2016) |
         (Grade == "10" & year == 2017) |
         (Grade == "11" & year == 2018) |
          (Grade == "12" & year == 2019) 
         ) %>%
  as_survey_design(strata = stratum, ids = psu, weight  = finwgt) %>%
    group_by(year) %>%
summarize(ecig_ever_year = survey_mean(ecig_ever, vartype = "ci", na.rm=TRUE),
            ecig_current_year = survey_mean(ecig_current, vartype = "ci", na.rm=TRUE),
            non_ecig_ever_year = survey_mean(non_ecig_ever, vartype = "ci", na.rm=TRUE),
            non_ecig_current_year = survey_mean(non_ecig_current, vartype = "ci", na.rm=TRUE)) %>%
  mutate_at(vars(-year), "*", 100) %>%
  dplyr::select(year,
                ecig_ever_year,
                ecig_current_year,
                non_ecig_ever_year,
                non_ecig_current_year,
                contains("low"),
                contains("upp")) %>%
  pivot_longer(cols = -year, names_to = "Category", values_to = "Percentage of students")%>%
    mutate(Estimate = case_when(grepl("_low", Category) ~ "Lower",
                          grepl("_upp", Category) ~ "Upper",
                          TRUE ~ "Mean"),
         User = case_when(grepl("ever", Category) ~ "Ever",
                          grepl("current", Category) ~ "Current"),
         Product = case_when(grepl("non_ecig", Category) ~ "Other products",
                          TRUE ~ "E-cigarettes")) %>%
  dplyr::select(-Category) %>%
  pivot_wider(names_from = Estimate, 
              values_from = `Percentage of students`) %>%
    ggplot(aes(x=year,y=Mean, color=Product)) +
  geom_line(aes(linetype=User)) +
  geom_linerange(aes(ymin = Lower, ymax = Upper)) + 
  scale_linetype_manual(values = c(2,1)) +
  scale_y_continuous(breaks = seq(0, 60, by = 10), limits = c(0,60)) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.title.x = element_blank()) +
    labs(title = "E-cigarette use surpasses use of other nicotine products",
         y = "% of students")

title_w_8 <- ggdraw() + 
  draw_label(
    expression("Among"~8^th~"graders in 2015, have vaping rates possibly influenced tobacco/nicotine use?"),
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_w_8 <- plot_grid(plotA_w_8,
                        rel_widths = c(1),
                        align = "v",
                        axis = "bt")

plotsBC_w_8 <- plot_grid(plotB_w_8,
                         plotC_w_8,
                         rel_widths = c(1,1),
                         axis = "bt")

legend_w_8 <- get_legend(plot8 +
                       theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

figure_w_8 <- plot_grid(title_w_8,
                        plotsA_w_8,
                        plotsBC_w_8,
                        legend_w_8,
                        ncol = 1,
                        rel_heights = c(0.1,
                                      1,
                                      1,
                                      0.1),
                        scale = 1.0
)

figure_w_8
```

### Final Figure

```{r}
title_final <- ggdraw() +
  draw_label(
    expression("What is the relationship between vaping rates and tobacco/nicotine use?"),
    fontface = 'bold',
    size=16,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_uw_final <- ggdraw() + 
  draw_label(
    expression(~6^th~"-"~12^th~"graders, unweighted"),
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_w_final <- ggdraw() + 
  draw_label(
    expression(~6^th~"-"~12^th~"graders, weighted"),
    fontface = 'bold',
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_w_8_final <- ggdraw() + 
  draw_label(
    expression(~8^th~"graders in 2015, weighted"),
    fontface = 'bold',
    size=12,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

subtitle_final <- plot_grid(subtitle_uw_final,
                            subtitle_w_final,
                            subtitle_w_8_final,
                            ncol = 3)

plotsA_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of e-cigarette use by user type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsA_final <- plot_grid(plotA_uw + theme(plot.title = element_blank()),
                          plotA_w + theme(plot.title = element_blank()),
                          plotA_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

plotsB_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of ever use by product type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsB_final <- plot_grid(plotB_uw + theme(plot.title = element_blank()),
                          plotB_w + theme(plot.title = element_blank()),
                          plotB_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

plotsC_title_final <- ggdraw() + 
  draw_label(
    expression("Prevalence of nicotine product use by product & user type"),
    size=14,
    x = 0.5) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plotsC_final <- plot_grid(plotC_uw + theme(plot.title = element_blank()),
                          plotC_w + theme(plot.title = element_blank()),
                          plotC_w_8 + theme(plot.title = element_blank()),
                          ncol = 3,
                          align = "v",
                          axis = "bt")

legend_final <- get_legend(plot8 +
                             theme(legend.position = "bottom",
                             legend.direction = "horizontal"))

final_plot <- plot_grid(title_final,
          plotsA_title_final,
          subtitle_final,
          plotsA_final,
          plotsB_title_final,
          subtitle_final,
          plotsB_final,
          plotsC_title_final,
          subtitle_final,
          plotsC_final,
          legend_final,
          ncol = 1,
          rel_heights = c(0.2,
                          0.2,
                          0.1,
                          1,
                          0.2,
                          0.1,
                          1,
                          0.2,
                          0.1,
                          1,
                          0.1))

final_plot
```

```{r, echo=FALSE, include=FALSE}
ggsave(here::here("img", "final_plot.png"))
```

## **Suggested Homework**
*** 

<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">

+ Apply survey weights to one of the figures produced in this case study in which weighted estimates were not produced. Include error bars in the updated figure.
    + Does the figure change after the application of survey weights?
    + If so, describe how. 
+ Reproduce `final_plot` above for a different cohort of your choice.

</div>

### Notes

Ever and current variables are limited to those shared by all years of data included in this case study.

```{r, echo=FALSE}
knit_time_end <- Sys.time()
```

```{r, echo=FALSE}
knit_time <- knit_time_end - knit_time_start
knit_time_message <- paste("Knit time:",
      round(as.numeric(knit_time) ,3),
      units(knit_time)
      )
```

<p style="color:red">New code: `r knit_time_message`. Previous code: ~ 3 m. </p>

### Problems

I had difficulty producing a plot that succinctly presented a trend. It's very easy to produce plots that are very useful once one is familiar with the data. Some plots, however, cannot stand alone and need additional context to be clear for those without prior knowledge of the data. When I first shared a plot I had been working on with others, it became clear that in my effort to present a complicated idea briefly I had left out information that would make the trend easily interpretable. To solve this issue, I began to present visualizations of the data alongside my original plot. The final figure I created contained several additional plots, each presenting the same trend at a different level than my initial plot.

My "centerpiece" plot is the middle plot in `final_plot`. The 8 plots around it help provide a very clear picture of what is going on in the US with regards to e-cigarette use and nicotine product use at large. On its own, it's difficult to understand the trends in the US and how important the weighting scheme is for inference. Once you add the left and right columns, it's clear what is going on. 



## **Data Analysis**
*** 

### **content header**
*** 



## **Summary**
*** 
 cool visualization that summarizes everything... could add this somewhere: https://www.fda.gov/tobacco-products/youth-and-tobacco/youth-tobacco-use-results-national-youth-tobacco-survey



## **Helpful Links**
*** 

review of [tidymodels](https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/){target="_blank"} 

guide for [preprocessing with recipes](http://www.rebeccabarter.com/blog/2019-06-06_pre_processing/)


## **Session info**
***

```{r}
sessionInfo()
```